[Feature]Use DispatchGmmCombineDecode operator to replace MC2(Optional) (#5040)

### What this PR does / why we need it?

This PR adds model-side integration for the previously introduced
experimental AscendC fused operator DispatchGmmCombineDecode, used in
MoE decoding.

The operator implementation itself was added in a prior PR[#4139
](https://github.com/vllm-project/vllm-ascend/pull/4139).
This change only adapts the model execution path to optionally use the
fused operator.

When the environment variable VLLM_ASCEND_ENABLE_FUSED_MC2=2 is set, the
original MC2 path composed of multiple operators (A8W8 dispatch → GMM →
SwiGLU → GMM → combine) might be replaced by the single fused operator
DispatchGmmCombineDecode.

By default, the existing multi-operator MC2 implementation is preserved.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangqiankun <wangqiankun13@huawei.com>

vllm_ascend/ops/fused_moe/token_dispatcher.py

@@ -125,6 +125,7 @@ class TokenDispatcherWithMC2(MoETokenDispatcher):
             max_num_tokens = min(max_num_reqs * uniform_decode_query_len, 512)
         num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
         self.global_bs = num_tokens_per_tp_rank * self.ep_world_size
+        self.fused_global_bs = max_num_tokens * self.ep_world_size
 
     def get_dispatch_mc2_kwargs(
         self,