Window function perf: Speed up rank window materialization

[Dandandan]

Which issue does this PR close?

• None.

Rationale for this change

rank, dense_rank, percent_rank, and cume_dist currently materialize rank output arrays through nested iterator pipelines using scan, repeat_n, and flatten. These paths run for every partition for rank-like window functions, so avoiding iterator layering helps both all-distinct and repeated peer-group partitions.

What changes are included in this PR?

• Adds a singleton peer-group fast path for rank-like window functions when every row has its own rank group.
• Replaces repeated peer-group iterator materialization with preallocated vector filling for rank, dense_rank, percent_rank, and cume_dist.
• Adds focused Criterion coverage for rank-family evaluate_all_with_rank materialization.

Short local Criterion run on 8192 rows after the latest update:

• singleton groups: rank 10.466 us -> 1.090 us, dense_rank 10.491 us -> 1.058 us, percent_rank 13.093 us -> 1.877 us, cume_dist 12.699 us -> 1.796 us
• group size 8: rank 7.971 us -> 3.975 us, dense_rank 7.208 us -> 3.890 us, percent_rank 8.910 us -> 4.176 us, cume_dist 6.157 us -> 4.108 us
• group size 64: rank 7.974 us -> 1.283 us, dense_rank 5.518 us -> 1.201 us, percent_rank 8.134 us -> 1.190 us, cume_dist 4.747 us -> 1.211 us

Benchmark command:

cargo bench -p datafusion-functions-window --bench rank -- --sample-size 10 --warm-up-time 1 --measurement-time 2

Are these changes tested?

Yes.

cargo fmt --all
cargo test -p datafusion-functions-window
cargo clippy --all-targets --all-features -- -D warnings
cargo bench -p datafusion-functions-window --bench rank -- --sample-size 10 --warm-up-time 1 --measurement-time 2

Are there any user-facing changes?

No API or behavior changes. This only changes internal array materialization for rank-like window functions.