[Feature] Create benchmark and dataset usage scripts for embedding models

Look at the following for related tests, and the benchmark script should be similar to bench_serving Existing Tests:

models/test_embedding_models.py — Backend, 73s — PR: core embedding model implementations (covers srt/models/*_embedding.py, srt/entrypoints/openai/serving_embedding.py).

models/test_encoder_embedding_models.py — Backend, 100s — Post-merge: encoder-based embedding models (BERT/Roberta-style).

models/test_cross_encoder_models.py — Backend, 100s — Post-merge: cross-encoder/reranker models (related to embedding-based tasks but a different architecture).

openai_server/basic/test_serving_embedding.py — Frontend/unit, 10s — Run on all PRs: embedding serving layer (validates the serving path).

openai_server/basic/test_openai_embedding.py — Frontend, 141s — Frontend (PR/post-merge as listed): end-to-end OpenAI embedding API endpoints.

test_input_embeddings.py — Backend, 38s — Post-merge: tests the input embedding handling/path.