[PR] [AURON #2574] Support native cumulative rows windows for `MIN`, `MAX` and `COUNT` [auron]

["lyne7-sc (via GitHub)" <gi...@apache.org>]

lyne7-sc opened a new pull request, #2575:
URL: https://github.com/apache/auron/pull/2575

   # Which issue does this PR close?
   
   Closes #2574
   
   # Rationale for this change
   
   `MIN`, `MAX` and `COUNT` windows fall back to Spark because native conversion does not handle their `AggregateExpression` wrapper. Enabling these windows also requires fixing the shared floating-point min/max implementation, which can produce incorrect results when `NaN` appears before or after an ordinary value.
   
   # What changes are included in this PR?
   
   - Unwrap non-distinct, unfiltered, Complete MIN/MAX/COUNT aggregates while retaining the existing cumulative ROWS frame restriction. 
   - Apply Spark-compatible `NaN` ordering during min/max updates and merges, preserving ordinary comparisons and signed-zero behavior. 
   - Add result-comparison tests for window aggregates, input types, NULL handling, batch boundaries and grouped floating-point aggregates.
   
   # Are there any user-facing changes?
   
   MIN/MAX/COUNT cumulative rows windows can execute natively when native window execution is enabled.
   
   # How was this patch tested?
   
   - Added `float_min_max_nan_update_and_merge` to verify floating-point MIN/MAX semantics during updates and merges. 
   - Extended `AuronWindowSuite` to compare cumulative ROWS windows and grouped aggregate results with Spark.
   
   # Was this patch authored or co-authored using generative AI tooling?
   - [x] Yes
   - [ ] No
   
   If yes, include: `Generated-by: OpenAI Codex (GPT-6)`
   
   ASF guidance: https://www.apache.org/legal/generative-tooling.html
   


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