[NPUW] Support prefill-chunk for text-embedding model (#33076)
### Details:
Qwen3-text-embedding is a transformer-based casual model and it's not
the traditional LLM and is not directly adapted to NPUW.
The benefits of prefill-chunk for `Qwen3-text-embedding`:
- support long context
- Performance improvement
Changes:
- Added KVCache nodes in model and updated shapes for related nodes.
- Added `positon_ids` input node since it's hardcoded in original model.
- Created a separate model to handle the post-processing.
- Cached the output of prefill since `mean` post-processing needs entire
output data.
Notes:
1. Though kvcache model is not needed at all, it's still there. As I
don't want to add many `if-else`. And the penalty is the compilation
time increasing.
2. Padding is only supported in the mean post-processing mode for now,
which makes thing simple. I can add the padding support on left in
following PRs if required.
3. GenAI PR: https://github.com/openvinotoolkit/openvino.genai/pull/3088
4. The
[tests](https://jira.devtools.intel.com/secure/attachment/5782028/text_embeddings.py)
has been verified to work with both NPUW and GenAI updates.
Update:
1. Introduced new files `embedding_model_utils.cpp and
embedding_model_utils.hpp` to encapsulate all embedding-related
functionality.
2. Added `embedding_infer_request.cpp and embedding_infer_request.hpp`
to implement the new request type `EmbeddingInferRequest`.
3. Created `llm_infer_base_request.hpp` as a common base class for
`llm-infer-request` and `embedding-infer-request`.
### Tickets:
- [CVS-177453](https://jira.devtools.intel.com/browse/CVS-177453)
