[BugFix][310p][Cherry-pick] Handle null quantization config in ShardedStateLoader310&[Feature][310P] Support W8A8 dynamic linear method (#8296)

### What this PR does / why we need it?
This PR implements the `AscendW8A8DynamicLinearMethod310` quantization
scheme specifically for 310P hardware. It includes the logic for weight
retrieval, per-channel parameter generation, and the application of
dynamic quantization using NPU-specific kernels. Additionally, it
updates `ShardedStateLoader310` to handle quantization configurations
more robustly when generating parameter type maps.

Feedback from the review identified two critical issues in the
implementation:
1. The tensor squeezing logic in the `apply` method incorrectly handles
2D inputs, which may lead to shape mismatches in subsequent layers.
2. The weight tensor in `process_weights_after_loading` is transposed
after being converted to the private NZ format; the transpose operation
should be performed on the ND tensor before conversion to ensure correct
physical layout.

cherry-pick from : #7546 #7725
### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
New unit tests were added in
`tests/ut/_310p/quantization/test_w8a8_dynamic_310.py` to verify the
quantization method, and
`tests/ut/_310p/test_sharded_state_loader_310p.py` was updated to test
the state loader changes.

---------

Signed-off-by: csoulnd <daidaicurry@foxmail.com>

vllm_ascend/_310p/worker_310p.py

@@ -48,7 +48,11 @@ class NPUWorker310(NPUWorker):
             max_size=max_size,
         )
 
-        ShardedStateLoader310.generate_quant_description(self.model_runner.model, path)
+        ShardedStateLoader310.generate_quant_description(
+            self.model_runner.model,
+            path,
+            self.vllm_config.quant_config,
+        )
 
     @torch.inference_mode()
     def determine_available_memory(self) -> int: