Turn on device memory planing as default

[Gasoonjia]

Stack from ghstack (oldest at bottom):

• [ET Device Support] CUDA-native Qwen 3.5 MoE inference with device tensor pipeline #18788
• -> Turn on device memory planing as default #20214
• [ET Device Support] Add device tensor helper functions to TensorPtr API #19828
• [ET Device Support] make device support config method-based #19582
• [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] Add device tensor helper functions to TensorPtr API #18761

This diff turn on the on-device memory planing as default, so that every delegate which enables on device memory planing will be use that./

Also update cuda backend to remove H2D/D2H copies and extra caches

Differential Revision: D107597774

[pytorch-bot]

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❌ 2 New Failures, 16 Pending, 1 Unrelated Failure

As of commit f44379b with merge base 14f2017 ():

NEW FAILURES - The following jobs have failed:

• pull / test-arm-backend-no-driver (test_pytest_ops_tosa) / linux-job (gh)
  RuntimeError: Command docker exec -t 8b1bcb00ae5c91c5175a58b8ff34a7d890e449bc0d21945d9ad5f0f8e7d64a83 /exec failed with exit code 1
• Test CUDA Windows Export and E2E / test-model-cuda-windows-e2e (facebook, dinov2-small-imagenet1k-1-layer, non-quantized) / windows-job (gh)
  Process completed with exit code 1.

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• pull / test-qnn-testsuite-linux / test-backend-linux (qnn, models) / linux-job (gh) (matched linux rule in flaky-rules.json)
  The runner has received a shutdown signal. This can happen when the runner service is stopped, or a manually started runner is canceled.

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