Update on "[ExecuTorch][to_backend] Enable passing Delegation Spec to to_backend"

Support Entire Graph Delegation Flow through EdgeProgramManager's to_backend.

### Motivation
A current usecase for backend lowering is through the `to_backend(backend_id, exported_program, compile_spec)` API which lowers the entire exported program to the specified backend_id. However, lowering via the EdgeProgramManager only allows for partitioner based lowering. the EdgeProgramManager is the main component which enables support for multiple methods, as a result backends which leverage the old `to_backend(backend_id, ...)` api can not export executorch models with multiple methods.

### Design
We override EdgeProgramManager to also allow Partitioner to be replaceable by DelegationSpec. DelegationSpec is essentially a wrapper around the backend_id and the compile_spec, so any where a partitioenr is specified to lower a graph, the delegation spec can also be used to do entier graph lowering.

### Intended Flow
```
del_spec = DelegationSpec("BackendWithCompilerDemo", [CompileSpec(...)])
encode_graph = torch.export.export(Encoder(), sample_inputs)
decode_graph = torch.export.export(Decoder(), sample_inputs)
edge_manager = to_edge({
    "encode": encode_graph,
    "decode": decode_graph,
})
lowered_edge_manager = edge_manager.to_backend(del_spec)
# or if you want to specify which methods to lower to with del_spec
lowered_edge_manager= edge_manager.to_backend({
    "encode": del_spec,
})
```

Differential Revision: [D69086565](https://our.internmc.facebook.com/intern/diff/D69086565/)

cc cccclai

[ghstack-poisoned]

Author: mcr229

exir/backend/test/test_backends.py

@@ -1278,7 +1278,6 @@ def __init__(self):
 
             def forward(self, x):
                 return [torch.sin(x)]
- 
 
         sin_module = SinModule()
         model_inputs = (torch.ones(1),)
@@ -1353,7 +1352,7 @@ def __init__(self):
 
             def forward(self, x):
                 return torch.sin(x)
- 
+
             def inputs(self):
                 return (torch.ones(1),)
 
@@ -1365,7 +1364,7 @@ def forward(self, a, x, b):
                 y = torch.mm(a, x)
                 z = torch.add(y, b)
                 return z
- 
+
             def inputs(self):
                 return (torch.ones(2, 2), 2 * torch.ones(2, 2), 3 * torch.ones(2, 2))