feat(sql): thread opportunity_score from location_opportunity_profile into pSEO serving chain

- dim_cities: add geoname_id to geonames_pop CTE and final SELECT
  Creates FK between dim_cities (city-with-padel-venues) and dim_locations (all GeoNames),
  enabling joins to location_opportunity_profile for the first time.
- city_market_profile: pass geoname_id through base CTE and final SELECT
- pseo_city_costs_de: LEFT JOIN location_opportunity_profile on (country_code, geoname_id),
  add opportunity_score to output columns
- pseo_country_overview: add avg_opportunity_score, top_opportunity_score, top_opportunity_slugs,
  top_opportunity_names aggregates

Cities with no GeoNames name match get opportunity_score = NULL; templates guard with
{% if opportunity_score %}.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

transform/sqlmesh_padelnomics/models/serving/pseo_country_overview.sql

@@ -23,6 +23,12 @@ SELECT
   -- Top 5 cities by market score for internal linking (DuckDB list slice syntax)
   LIST(city_slug ORDER BY market_score DESC NULLS LAST)[1:5]     AS top_city_slugs,
   LIST(city_name ORDER BY market_score DESC NULLS LAST)[1:5]     AS top_city_names,
+  -- Opportunity score aggregates (NULL-safe: cities without geoname_id match excluded from AVG)
+  ROUND(AVG(opportunity_score), 1)                               AS avg_opportunity_score,
+  MAX(opportunity_score)                                         AS top_opportunity_score,
+  -- Top 5 cities by opportunity score (may differ from top market score cities)
+  LIST(city_slug ORDER BY opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_slugs,
+  LIST(city_name ORDER BY opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_names,
   -- Pricing medians across cities (NULL when no Playtomic coverage in country)
   ROUND(MEDIAN(median_hourly_rate), 0)                           AS median_hourly_rate,
   ROUND(MEDIAN(median_peak_rate), 0)                             AS median_peak_rate,