Merged PR 4926: KPIs Refactor Stage 3 - Creates dedicated Churn model in KPIs

# Description

Creates a new model in intermediate/kpis called `int_kpis__agg_monthly_churn_contribution`. This follows the convention (aggregated = by dimension and dimension value) and monthly (1 dimension value per dimension per date where date is unique in month).

This single model aims to substitute, in the future, the 2 churn models simultaneously:
* `int_monthly_12m_window_contribution_by_deal`
* `int_monthly_churn_metrics`

Main changes:
* There's no longer a dependency with `int_monthly_aggregated_metrics_history_by_deal`. Rather, it gets the metrics and dimensions from wherever needed and nothing else.
* I also took the opportunity to clean any code that was not needed after combining both models, resulting in dropping a whole CTE.
* Updated schema description for clarification purposes.

The output of the new model is identical to the 2nd model, `int_monthly_churn_metrics`, confirmed with the md5 trick. Model runs in 1.45 seconds in my laptop so no performance issue (it's technically faster than running the 2 current models which is around 6.3 seconds but whatever).

Note that this has NO effect yet on production. The switch will be handled in a separated PR.

# Checklist

- [X] The edited models and dependants run properly with production data.
- [X] The edited models are sufficiently documented.
- [X] The edited models contain PK tests, and I've ran and passed them.
- [X] I have checked for DRY opportunities with other models and docs.
- [X] I've picked the right materialization for the affected models.

# Other

- [ ] Check if a full-refresh is required after this PR is merged.

Related work items: #28948

models/intermediate/kpis/schema.yml

@@ -8624,3 +8624,80 @@ models:
         data_type: bigint
         description: The daily created bookings for a given date, dimension and value.
 
+  - name: int_kpis__agg_monthly_churn_contribution
+    description: |
+      This model calculates monthly churn contributions by dimension, dimension_value, 
+      and date. Unlike typical KPI models, it relies exclusively on monthly metrics 
+      computed at deal level and does not include data for the current month.
+
+      At its core, the model computes each deal's 12-month rolling contribution to global 
+      metrics. Afterwards, it aggregates these contributions for deals classified as 
+      '05-Churning' within the month. The output includes three churn-related contribution 
+      metrics, expressed as ratios over global totals:
+      - total_revenue_churn_average_contribution
+      - created_bookings_churn_average_contribution 
+      - listings_booked_in_month_churn_average_contribution 
+      Besides these 3 metrics, the actual total revenue churned in the month and the global 
+      total revenue windows are also included in the output. This is later used for YTD/MTD 
+      dedicated models, which only require it for Total Revenue.
+
+      These are calculated using an average contribution approach over the prior 12 months.
+      If a deal has not been active for the full 12-month period, the average is still 
+      computed based on the number of months the deal has been active within that window.
+
+      Note: When analysing dimensions other than 'global', the metrics represent the additive 
+      share of churn relative to the global total. For example, if the total churn rate in a 
+      month is 10%, it may be broken down as 9% from the USA and 1% from GBR — still totaling 10%.
+
+    data_tests:
+      - dbt_utils.unique_combination_of_columns:
+          combination_of_columns:
+            - date
+            - dimension
+            - dimension_value
+
+    columns:
+      - name: date
+        data_type: date
+        description: The date for the month-to-date metrics.
+        data_tests:
+          - not_null
+
+      - name: dimension
+        data_type: string
+        description: The dimension or granularity of the metrics.
+        data_tests:
+          - accepted_values:
+              values:
+                - global
+                - by_number_of_listings
+                - by_billing_country
+
+      - name: dimension_value
+        data_type: string
+        description: The value or segment available for the selected dimension.
+        data_tests:
+          - not_null
+
+      - name: total_revenue_churn_preceding_12_months
+        data_type: numeric
+        description: |
+          Total Revenue attributed to have churned considering the 
+          revenue generated by the deals in the 12 months period.
+
+      - name: total_revenue_global_preceding_12_months
+        data_type: numeric
+        description: |
+          Total Revenue generated by all deals in the 12 months period.
+
+      - name: total_revenue_churn_average_contribution
+        data_type: numeric
+        description: Total Revenue churn rate (average approach).
+
+      - name: created_bookings_churn_average_contribution
+        data_type: numeric
+        description: Created Bookings churn rate (average approach).
+
+      - name: listings_booked_in_month_churn_average_contribution
+        data_type: numeric
+        description: Listings Booked in Month churn rate (average approach).