What this PR does / why we need it

Adds an opt-in filter_by_created_timestamp: bool = False parameter to get_historical_features that enforces created_timestamp <= entity_timestamp in point-in-time joins.

Today created_timestamp_column is used only as a dedup tiebreaker, never as a filter, so a training row at entity timestamp T can be served a value that did not exist yet at T (backfills, late corrections). With filter_by_created_timestamp=True, retrieval reflects what was actually known as of each entity row's timestamp, i.e. what the online store would have served at that moment.

store.get_historical_features(
    entity_df=entity_df,
    features=features,
    filter_by_created_timestamp=True,  # default False: existing behavior
)

Design

• Threaded through FeatureStore.get_historical_features using the same optional-kwargs pattern as start_date/end_date: only passed to the offline store when True, so the default path is untouched and third-party stores are unaffected unless a user explicitly opts in.
• For SQL-template stores, the shared build_point_in_time_query gains a filter_by_created_timestamp template variable, and each template's __base join gains one guarded predicate:

  {% if filter_by_created_timestamp and featureview.created_timestamp_column %}
  AND subquery.created_timestamp <= entity_dataframe.entity_timestamp
  {% endif %}

  Everything else (event-timestamp window, TTL, dedup, ROW_NUMBER) is unchanged.
• Feature views without a created_timestamp_column are unaffected.

Store coverage

Implemented: BigQuery, Redshift, Spark, Trino, Athena, Postgres, ClickHouse, Couchbase (SQL templates); DuckDB, MSSQL, Oracle (via the shared ibis point_in_time_join); Dask/file (pandas filter mirroring _filter_ttl); Hybrid (delegates, re-checking the resolved child store).

Stores declare support via a supports_filter_by_created_timestamp class attribute on OfflineStore (default False). A single guard in the passthrough provider raises NotImplementedError when the flag is set for a store that does not declare support, so no store can silently ignore it and new stores are covered automatically. Unsupported today: Snowflake (its ASOF JOIN dedups before the join and MATCH_CONDITION takes a single predicate, so the cutoff cannot be expressed without restructuring the query), Remote (needs protocol support to forward the flag), Ray, and MongoDB.

Notes

• Rows with a NULL created_timestamp are excluded by the predicate in all implementations; the docstring notes the column should be non-null when the mode is used. (In the dask store, unmatched entity rows from the left join are still preserved.)
• In the dask store, an entity row whose matching feature rows are all filtered out disappears from the result instead of surviving with null features. This matches the store's existing TTL filtering behavior and is a pre-existing trait of its incremental join, not something this PR changes.
• Orthogonal to ttl: TTL bounds by event time, this bounds by known time.

Which issue(s) this PR fixes

Fixes #6615

Misc

Tests added in sdk/python/tests/unit/infra/offline_stores/test_filter_by_created_timestamp.py:

• Template rendering: predicate present iff filter_by_created_timestamp=True and a created_timestamp_column is set; default rendering unchanged.
• Dask and ibis (DuckDB) end-to-end: same event timestamp with two versions created before/after the entity timestamp; default serves the later-created value, filter_by_created_timestamp=True serves the version known at the entity timestamp.

ruff check/ruff format clean; mypy feast reports the same 17 pre-existing errors as master (none introduced).