feat(openai): concurrent batch API calls in async embedding methods

### Checked other resources - [x] This is a feature request, not a bug report or usage question. - [x] I added a clear and descriptive title that summarizes the feature request. - [x] I used the GitHub search to find a similar feature request and didn't find it. - [x] I checked the LangChain documentation and API reference to see if this feature already exists. - [x] This is not related to the langchain-community package. ### Package (Required) - [ ] langchain - [x] langchain-openai - [ ] langchain-anthropic - [ ] langchain-classic - [ ] langchain-core - [ ] langchain-model-profiles - [ ] langchain-tests - [ ] langchain-text-splitters - [ ] langchain-chroma - [ ] langchain-deepseek - [ ] langchain-exa - [ ] langchain-fireworks - [ ] langchain-groq - [ ] langchain-huggingface - [ ] langchain-mistralai - [ ] langchain-nomic - [ ] langchain-ollama - [ ] langchain-openrouter - [ ] langchain-perplexity - [ ] langchain-qdrant - [ ] langchain-xai - [ ] Other / not sure / general ### Feature Description Description (suggested): ### Feature `OpenAIEmbeddings._aget_len_safe_embeddings` and the `aembed_documents` fast path currently process batch API calls sequentially — each batch awaits the previous one. For large document sets (e.g., 5000 docs with chunk_size=1000), this means 5 serial HTTP round-trips. ### Proposal Replace the sequential `while/await` loops with `asyncio.gather` to fire all batch API calls concurrently. This follows the same pattern already used by `MistralAIEmbeddings.aembed_documents`. The change is limited to `libs/partners/openai/langchain_openai/embeddings/base.py`. No changes to the `Embeddings` ABC or any vector store. Every async consumer benefits automatically. ### Motivation Near-linear speedup for large document ingestion via `aadd_documents` on any vector store using OpenAI embeddings. ### Use Case ### Use Case **RAG document ingestion at scale** When building a RAG (Retrieval-Augmented Generation) pipeline, a common step is embedding …