[Feat] 310p support MoE W8A8 quantizaition (#6641)

### What this PR does / why we need it?
This PR introduces support for W8A8 dynamic quantization for
Mixture-of-Experts (MoE) models on Ascend 310P devices. This is achieved
by:
- Implementing a new quantization scheme
`AscendW8A8DynamicFusedMoEMethod310`.
- Adding a unified MLP implementation (`unified_apply_mlp`) for 310P
that handles both quantized and unquantized paths.
- Refactoring the MoE and quantization configuration logic to correctly
route to the new 310P-specific implementations.
- Adding new e2e and unit tests to verify the functionality of MoE W8A8
quantization.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
- Added a new e2e test `test_qwen3_moe_tp2_w8a8` to test MoE W8A8
quantization in a multi-card setup.
- Added several new unit tests for the 310P-specific MoE components,
including `experts_selector`, `fused_moe`, `moe_comm_method`, `moe_mlp`,
and the new `w8a8_dynamic` quantization method.

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

tests/e2e/310p/multicard/test_moe_model_multicard.py

@@ -44,3 +44,17 @@ def test_qwen3_moe_ep4_fp16():
             enable_expert_parallel=True
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+def test_qwen3_moe_tp2_w8a8():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            "vllm-ascend/Qwen3-30B-A3B-W8A8",
+            tensor_parallel_size=2,
+            enforce_eager=True,
+            dtype="float16",
+            quantization="ascend"
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)