perf: optimize date_trunc in datafusion-functions

[andygrove]

This PR was created by an LLM as a draft PR. I will mark it as ready for review after human review.

Which issue does this PR close?

N/A — autonomous exploratory PR.

Rationale for this change

Replaced the per-row chrono NaiveDateTime round trip in the untimezoned coarse path of date_trunc (week/month/quarter/year) with integer civil-calendar arithmetic, cutting several allocationless-but-validating chrono field setters down to a few integer ops per row.

What changes are included in this PR?

Replaced the per-row chrono NaiveDateTime round trip in the untimezoned coarse path of date_trunc (week/month/quarter/year) with integer civil-calendar arithmetic, cutting several allocationless-but-validating chrono field setters down to a few integer ops per row.

Are these changes tested?

Correctness: unit tests + seeded differential fuzz (bit-identical Arrow output vs main).

Benchmark (criterion):

• date_trunc_month_nanos_1000: 48.452% faster (base 19164ns -> cand 9878ns)
• date_trunc_week_nanos_1000: 76.045% faster (base 23337ns -> cand 5590ns)
• date_trunc_month_second_1000: 47.688% faster (base 17376ns -> cand 9090ns)
• date_trunc_quarter_nanos_1000: 32.493% faster (base 22005ns -> cand 14854ns)
• date_trunc_year_nanos_1000: 39.449% faster (base 20460ns -> cand 12388ns)
• date_trunc_minute_1000: 1.2% faster (base 654ns -> cand 647ns)

Full criterion output:

date_trunc_minute_1000  time:   [645.50 ns 652.66 ns 659.52 ns]
                        change: [−2.0837% −1.1996% −0.2678%] (p = 0.01 < 0.05)
                        Change within noise threshold.
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) high mild

date_trunc_month_second_1000
                        time:   [9.0984 µs 9.1139 µs 9.1298 µs]
                        change: [−47.773% −47.688% −47.597%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe

date_trunc_week_nanos_1000
                        time:   [5.5882 µs 5.6103 µs 5.6308 µs]
                        change: [−76.154% −76.045% −75.951%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 2 outliers among 100 measurements (2.00%)
  2 (2.00%) low mild

date_trunc_month_nanos_1000
                        time:   [9.8640 µs 9.8831 µs 9.9059 µs]
                        change: [−48.576% −48.452% −48.309%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 4 outliers among 100 measurements (4.00%)
  3 (3.00%) high mild
  1 (1.00%) high severe

date_trunc_quarter_nanos_1000
                        time:   [14.849 µs 14.857 µs 14.869 µs]
                        change: [−32.666% −32.493% −32.339%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 16 outliers among 100 measurements (16.00%)
  1 (1.00%) low severe
  2 (2.00%) low mild
  7 (7.00%) high mild
  6 (6.00%) high severe

date_trunc_year_nanos_1000
                        time:   [12.385 µs 12.391 µs 12.398 µs]
                        change: [−39.498% −39.449% −39.405%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
  2 (2.00%) low mild
  4 (4.00%) high mild
  3 (3.00%) high severe

Are there any user-facing changes?