[CoreML EP] Support bool Cast in ML Program

[maxwbuckley]

Summary

Two changes to the ML Program Cast builder:

1. Accept BOOL as a source and target dtype in HasSupportedInputsImpl. The
   ML Program cast op already handles bool, and AddToModelBuilderImpl already
   maps to == BOOL; only the input/output type gate omitted it.
2. Move the "no preceding node" check after the ML Program early-return. That
   check is legacy gating for the NeuralNetwork ArgMax-only path (which
   dereferences InputEdgesBegin()); on the ML Program path a Cast fed directly
   by a graph input is fine, and rejecting it forced needless CPU fallback.

Why

This is the first of a 4-PR series giving the CoreML EP the op coverage to run
transformer and diffusion graphs as a single CoreML partition instead of
fragmenting across CPU.

Transformer attention-mask graphs are a Cast → GatherND → And → Where chain over
bool tensors. A CoreML partition cannot have a bool input/output (CoreML
MLMultiArray has no bool type), so bool must stay internal — which makes Cast
(the int↔bool boundary) the prerequisite for the rest of the series.

Combined impact of the series

With all four PRs plus #28278 (scalar-Gather), every model below goes from 2
CoreML partitions to 1, with zero graph breaks — the whole graph runs on
CoreML. Measured on an Apple M3 Max, ML Program format:

Model	partitions (before → after)	CoreML vs CPU
BERT-large (340M)	2 → 1	7.3× (fp32) / 11.0× (fp16)
ViT-large (304M)	2 → 1	8.5× (fp32) / 10.3× (fp16)
GPT-2-large (774M)	2 → 1	11.4× (fp16)
SD-1.5 UNet (860M)	2 → 1	9.7× (fp16)

The op builders eliminate the graph breaks (deterministic); the speedups are what
CoreML already delivers once a model is no longer fragmented.

Tests (coreml_basic_test.cc)

• CastNonArgMaxNeuralNetworkNotSupported — an int64 → bool → float cast chain
  falls back to CPU on the NeuralNetwork format, guarding the IsOpSupportedImpl
  reordering.

Positive bool-Cast coverage is in the dependent PRs: Cast → GatherND → Cast
(#28598's GatherNDBoolData_MLProgram) and Cast → And → Cast (#28597's
And_MLProgram). Both place a non-Cast op between the int↔bool casts and check
the result against the CPU EP. A standalone int64 → Cast(bool) → Cast(float)
round-trip can't be verified here — CoreML's compiler fuses back-to-back cast
ops and drops the bool clamp — so the pattern needs that intervening op, which
only the dependent PRs provide.

Series — CoreML EP coverage for transformer / diffusion graphs

• [CoreML EP] Support bool Cast in ML Program #28595 — Support bool Cast in ML Program (this PR — prerequisite)
• [CoreML EP] Add Sin and Cos unary ops #28596 — Add Sin and Cos unary ops (independent)
• [CoreML EP] Add Where and And builders #28597 — Add Where and And builders (depends on [CoreML EP] Support bool Cast in ML Program #28595)
• [CoreML EP] Add GatherND builder #28598 — Add GatherND builder (depends on [CoreML EP] Support bool Cast in ML Program #28595)

Together with #28278 (scalar-Gather), the series takes BERT / GPT-2 / ViT /
diffusion-UNet graphs — tiny and full-size — from 2 CoreML partitions to 1, with
zero graph breaks.