[python] Support window joins in temporal alignment - #9760

Purpose

Join higher-frequency samples, such as IMU, to temporal anchor rows and aggregate each matched window.

Changes

• add top-level and chainable join_window
• support group-local preceding / following ranges with configurable endpoint closure
• support named outputs and multiple aggregations per source column
• support mean, min, max, first, last, and count; the latter three also accept non-numeric values
• prune right payload reads to aggregation columns
• reuse snapshot pinning, authorization masking, batched row-ID fetches, and the bounded row-group cache

API choice

rolling().agg() aggregates windows within one input. This operation uses each left timestamp to select rows from a right input, then aggregates the matches, so it follows established window-join terminology. The verb-first join_window also matches the public join_asof API.

Named (source, operation) pairs allow one source column to produce multiple outputs; same-name operation strings remain shorthand.

Tests

147 temporal and multimodal table tests passed on Python 3.11 / PyArrow 19, including regressions for preserving masked field types after pruning colliding projection aliases. The regressions cover mean after STRING-to-DOUBLE masking, first/last after INT-to-STRING masking, unmatched windows, and rejection of mean/min/max on masked strings. Both integer-boundary regression tests also passed on Python 3.6 / NumPy 1.19.5 / PyArrow 6.0.1. An additional 2,400 boundary cases passed on each of NumPy 1.19.5 and 2.2.6. Python 3.6 syntax and flake8 checks passed.

The masked-alias regressions cannot complete on PyArrow 6 because the existing masking reader requires RecordBatch.set_column, which is unavailable in that version.