[ET Device Support] Add NonConstBufferDevice schema for per-buffer device mapping

[Gasoonjia]

Stack from ghstack (oldest at bottom):

• [ET Device Support] CUDA-native Qwen 3.5 MoE inference with device tensor pipeline #18788
• [ET Device Support] Extract shared device test utilities to reduce redundancy #18762
• [ET Device Support] Add device tensor helper functions to TensorPtr API #18761
• [ET Device Support] Add ExecutorchBackendConfig flags for skipping H2D/D2H copies #18760
• [ET Device Support] PropagateDevicePass inserts H2D/D2H copy ops at delegate boundaries #18730
• [ET Device Support] Define et_copy runtime h2d and d2h copy ops #18729
• [ET Device Support] Define AOT device copy ops registry #18728
• [ET Device Support] CudaAllocator: device memory allocator for CUDA backend #18477
• [ET Device Support] Module: allocate device memory for planned buffers #18476
• [ET Device Support] MemoryManager: add per-buffer device metadata #18475
• [ET Device Support] MethodMeta: expose per-buffer device placement API #18474
• [ET Device Support] DeviceMemoryBuffer RAII class for device memory lifetime management #18473
• [ET Device Support] Emitter reads non_const_buffer_device from graph meta #18472
• [ET Device Support] Device-aware memory planning: separate buffers per device type #18375
• -> [ET Device Support] Add NonConstBufferDevice schema for per-buffer device mapping #18330
• [ET Device Support] DeviceAllocator interface and DeviceAllocatorRegistry #17535
• [ET Device Support] Annotate device attributes of CUDA backend IO tensors cuda device #18080
• [ET Device Support] Parse device info from serialized tensor in tensor_parser #18328
• [ET Device Support] Propagate device info from TensorSpec into serialized Tensor #18079
• [executorch] Propagate device metadata from partitioner result onto TensorSpecs #18078

Adds the NonConstBufferDevice table to the FlatBuffer schema (program.fbs) and the
corresponding Python dataclass to schema.py. This enables mapping each non-constant
planned memory buffer to a specific device type (CPU, CUDA, etc.).

The field is optional and absent for CPU-only programs, ensuring zero binary size regression.

Differential Revision: D97335597

[pytorch-bot]

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✅ You can merge normally! (2 Unrelated Failures)

As of commit b4c5f13 with merge base b5ae0b9 ():

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / unittest-editable / windows / windows-job (gh) (trunk failure)
  ##[error]The operation was canceled.

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