[Story][FEA] Complete grouped over expression coverage in cuDF-Polars

[rjzamora]

Grouped over(...) expressions are implemented in cuDF-Polars using a forward-shuffle / local-evaluate / backward-shuffle execution model. This issue tracks remaining grouped-over API coverage that can use that model before we tackle long-term huge-group execution where one group may need to span multiple partitions/ranks.

Initial child issues / feature areas:

• Support rolling_*_by(...).over(...): [FEA] Support rolling-by expressions inside over() in cuDF-Polars #23591
• Support rolling(...).over(...): [FEA] Support aggregate rolling expressions inside over() in cuDF-Polars #23623
• Support cumulative grouped-over variants such as cum_min().over(...), cum_max().over(...), cum_prod().over(...), and cum_count().over(...)
• Support pct_change().over(...)
• Reduce grouped-over boilerplate by sharing implementation/decomposition logic across order-sensitive expressions

Example:

def test_cum_max_over(engine: GPUEngine) -> None:
    lf = pl.LazyFrame(
        {
            "g": ["A", "A", "A", "B", "B"],
            "t": [1, 2, 3, 1, 2],
            "x": [10, 30, 20, 5, 15],
        }
    )

    q = lf.select(
        pl.col("x").cum_max().over("g", order_by="t").alias("x_cum_max")
    )
    assert_gpu_result_equal(q, engine=engine)

Note: This issue is about near-term grouped over(...) API coverage. Full distributed execution for low-cardinality / huge-group cases can be tracked separately under the broader grouped range-window or streaming ordered-window work.