Arm backend: Refactor scalar handling

[Erik-Lundell]

Arm backend: use scalars instead of fulls in TFA

Scalars are then converted to buffers by the
ScalarsToAttribute pass. This both simplifies the code,
and allows affected ops to be moved to device with
model.to(device=...).

Note that this does not solve all issues with device
kwargs after TFA, only specifically for scalar cases.

Arm backend: Clean up some pass inefficiencies.

- The ScalarToAttribute pass went through all submodules
for each node, it only needs to do it once.
- Some exir passes used full_like for scalars. This creates
very buffers of the same size as the input, when a single
value is enough.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell

[pytorch-bot]

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❌ 1 Cancelled Job, 1 Pending, 5 Unrelated Failures

As of commit 2087f35 with merge base f7977a6 ():

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• trunk / test-models-macos-coreml (w2l) / macos-job (gh)
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• pull / unittest-editable / macos / macos-job (gh) (similar failure)
  export/tests/test_target_recipes.py::TestTargetRecipes::test_mv3_model

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

• pull / unittest / macos / macos-job (gh) (trunk failure)
  export/tests/test_target_recipes.py::TestTargetRecipes::test_mv3_model
• 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.
• trunk / unittest-release / windows / windows-job (gh) (trunk failure)
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[Erik-Lundell]

Unrelated failures, merging.