Fix additive attention mask handling in the native NPU attention backend.

[mengchengTang]

What does this PR do?

Fixes attention mask handling in the native NPU attention backend.

PyTorch SDPA defines two mask formats:

• Boolean masks are keep masks, where True means attend.
• Floating-point masks are additive biases applied directly to attention scores.

Previously, _native_npu_attention passed every mask to _maybe_modify_attn_mask_npu, which casts the mask to boolean and inverts it for npu_fusion_attention. This works for boolean masks but loses the values and semantics of floating-point additive masks.

This PR makes the mask handling explicit:

• Floating-point masks are routed through the existing native SDPA implementation, preserving their additive values.
• Boolean masks continue using npu_fusion_attention.
• Other mask dtypes are rejected with a clear error instead of being silently converted to boolean.

No new dependencies are introduced.

Fixes #14397

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