Fix TensorPrimitives MinNumber/MaxNumber span reductions propagating NaN (#133346)

[jabrailkhalil]

Summary

Fixes #133346

TensorPrimitives.MinNumber(ReadOnlySpan<T>) (and MaxNumber, MinMagnitudeNumber, MaxMagnitudeNumber) returned NaN as soon as the reduction encountered a NaN, while the scalar T.MinNumber/T.MaxNumber semantics ignore a NaN operand when a numeric one is available (IEEE 754:2019 minimumNumber/maximumNumber).

The span reductions share MinMaxCore<T, TMinMaxOperator> with the plain Min/Max/Magnitude reductions, and that core early-exits on the first NaN at every vector width and in its scalar tail. The early exit is correct for minimum/maximum (which propagate NaN), but wrong for the *Number family.

Changes

• IAggregationOperator<T> gains static virtual bool PropagatesNaNs => true.
• The four *NumberOperator<T> structs override it with false.
• MinMaxCore gates its NaN early-exits on TMinMaxOperator.PropagatesNaNs (Vector512/256/128 paths plus the scalar tail), so *Number reductions proceed and the lane-wise *Number operator ignores the NaN, while plain Min/Max/Magnitude behavior is unchanged.
• Regression test NumberAggregates_IgnoreNaN in TensorPrimitives.Generic.cs: NaN at the start/middle/end of spans of several lengths (scalar and vector paths) for all four *Number reductions, the all-NaN case, plus signed-zero ordering and the plain Min/Max NaN propagation control.

Validation

Reproduced on the released .NET 10 SDK (10.0.401): TensorPrimitives.MinNumber<float>([1f, NaN, 2f]) returned NaN, and the same for MaxNumber, MinMagnitudeNumber, MaxMagnitudeNumber.

The fixed sources were compiled with the SDK (all src/System.Numerics.Tensors netcore sources) and verified against a 25-case matrix:

• Before the fix (same harness against the unmodified sources): MinNumber(float) [1, NaN, 2] FAILED with got NaN, expected 1.
• After the fix: all 25 cases pass - MinNumber/MaxNumber/MinMagnitudeNumber/MaxMagnitudeNumber ignore NaN (float, double, Half; NaN first/middle/last; vector-sized and scalar inputs), all-NaN inputs still return NaN, -0 vs +0 ordering preserved, and plain Min/Max/MinMagnitude/MaxMagnitude still propagate NaN.

Not run locally: the repo's full library test suite (requires the arcade toolchain setup). The regression test follows existing conventions in TensorPrimitives.Generic.cs (GenericFloatingPointNumberTensorPrimitivesTests<T>) and runs on all four floating-point instantiations in CI.

Note: the .NET Foundation CLA must be signed for this PR to be mergeable (the account jabrailkhalil will need to sign at https://cla.dotnetfoundation.org).

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