[NPUW] Fix multiple MatMul matching issue in NPUW LM head cutting (#32475)

### Details:
**Background:**
Eagle 3 pipeline will add new output in target model to get the
intermedium feature embeddings.

The `cut_lm_head` function separates the vocabulary matrix (LM head)
from LLM models for efficient inference. It needs to identify the
correct `MatMul` operation among multiple candidates in the model graph.

**Problem:**
When multiple `MatMul` operations match the pattern (common in LLMs),
the callback executes multiple times, with each execution overwriting
the previous result. Only the last matched `MatMul` is used, often
missing the actual vocabulary matrix.

**Solution:**
Replaced `MatcherPass` with direct traversal and intelligent selection:
1. Collect all candidates instead of using last match
2. Select MatMul with largest matrix size (vocabulary size heuristic)
3. Optimize traversal - iterate Result nodes directly instead of all
nodes


### Tickets:
 - [*CVS-175198*](https://jira.devtools.intel.com/browse/CVS-175198)

Author: GuoliangShiIntel

src/plugins/intel_npu/src/plugin/npuw/llm_compiled_model.cpp

@@ -881,6 +881,14 @@ class CutLMHead : public ov::pass::MatcherPass {
             auto matched_matmul = std::static_pointer_cast<ov::op::v0::MatMul>(matched_node_matmul);
             auto matched_result = std::static_pointer_cast<ov::op::v0::Result>(matched_node_result);
 
+            // Some LLMs add intermediate hidden state outputs that can interfere with LM head detection.
+            // Skip Result nodes that were manually added (marked with "manually_added_output" in RT_INFO).
+            // For example, Eagle-3 target/draft models add "last_hidden_state" output which should be skipped.
+            const auto& rt_info = matched_result->get_rt_info();
+            if (rt_info.count("manually_added_output")) {
+                return false;
+            }
+
             // Cut point:
             auto matmul_first_source = matched_matmul->input(0).get_source_output();