fix: only propagate cast statistics through safe conversions

[haohuaijin]

Which issue does this PR close?

• Closes CAST statistics can produce incorrect MIN/MAX results #25221.

Rationale for this change

MIN/MAX over a cast can return incorrect results when aggregate optimization replaces the scan with converted column statistics. Casting the original endpoints is only valid if they remain extrema in the target domain.

For example, a Parquet string column containing ('1', '100', '2') has string extrema '1' and '2'. Previously, MIN(CAST(a AS INT)), MAX(CAST(a AS INT)) could return 1, 2 from those statistics instead of the correct 1, 100. The same issue affects BIGINT.

What changes are included in this PR?

• Restrict CAST statistics propagation to conversions proven safe for the source type or its exact integer bounds. Unsupported or unsafe conversions return unknown column statistics, so aggregates evaluate the data.
• Preserve the existing same-type passthrough and supported lossless widening conversions.
• Retain integer narrowing and signed/unsigned conversions when both original bounds are exact and non-null, and both converted endpoints are non-null. Successful endpoint conversions establish that the entire integer range fits in the target type; overflow errors or null endpoints do not qualify.
• Extend CastExpr::check_bigger_cast to recognize Int32 ↔ Date32, and clarify that its contract includes lossless, strictly order-preserving conversions that preserve nulls. Arrow reinterprets the same i32 values as days since the epoch, so these conversions preserve both statistics and ordering properties. This also retains the statistics-based MIN/MAX optimization for ClickBench's UInt16 → Int32 → Date32 projection.

What is the testing strategy for this PR?

• Add projection unit tests for non-monotonic string-to-integer casts and integer narrowing with safe, overflowing, and inexact bounds. Removing the statistics guard makes the new string-cast regression test fail.
• Add end-to-end Parquet regression cases in parquet_statistics.slt for INT and BIGINT, both expecting 1, 100.
• Add a bidirectional Int32 ↔ Date32 test covering i32::MIN, i32::MAX, negative values, zero, NULL, and strict ordering properties.
• Run the related projection and cast unit tests, plus the ClickBench and Parquet statistics SLT files. The existing ClickBench plan expectation passes unchanged, confirming that its aggregate still folds to constants.

Local formatting, Clippy, license, spelling, workflow, and documentation checks also pass.

Are there any user-facing changes?

Affected casts now produce correct MIN/MAX results. Conversions whose safety is not established may require a scan instead of using statistics-based aggregate optimization. Supported safe integer conversions and Int32 ↔ Date32 retain that optimization.

[haohuaijin]

check_bigger_cast is already used for ordering inference. I added Int32 ↔ Date32 because Arrow reinterprets the same underlying values, preserving ordering and nulls. This also keeps ClickBench's MIN/MAX statistics optimization working after this PR restricts cast statistics propagation. Should we rename the helper to reflect its broader contract than widening?

[haohuaijin]

We can extend this helper in a follow-up to recognize more safe conversions, such as UInt32 → Int64, allowing statistics to be preserved without requiring exact bounds on both ends.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.28%. Comparing base (b300cea) to head (a1bed84).

Additional details and impacted files

@@           Coverage Diff            @@
##             main   #25227    +/-   ##
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  Coverage   82.28%   82.28%            
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  Files        1137     1137            
  Lines      430211   430339   +128     
  Branches   430211   430339   +128     
========================================
+ Hits       354018   354125   +107     
- Misses      54771    54788    +17     
- Partials    21422    21426     +4     

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[haohuaijin]

Hi @alamb @Dandandan could you help review this when you get a chance?