Feature Request: Add Milvus Vector Store Integration for RAG

### Description

Add [Milvus](https://milvus.io/) as a vector database option for knowledge base RAG workflows, following the existing Spanner integration pattern.

### Motivation

ADK currently supports Vertex AI RAG, LlamaIndex, and Spanner for vector-based retrieval. Milvus is a widely adopted open-source vector database purpose-built for AI applications. Adding Milvus support would give developers a lightweight, self-hosted (or cloud-managed via [Zilliz](https://zilliz.com/)) alternative for building RAG agents.

### Proposed Solution

- `MilvusVectorStore`: Utility class for collection setup, data ingestion, and similarity search.
- `MilvusToolset`: `BaseToolset` implementation exposing a `similarity_search` tool to LLM agents.
- `MilvusToolSettings` / `MilvusVectorStoreSettings`: Pydantic configuration classes.
- Users provide their own embedding function (e.g., Google GenAI `gemini-embedding-001`).
- `pymilvus>=2.5.0` as an optional dependency (`google-adk[milvus]`).

### Related PR

google/adk-python#4417

Start by reading the existing Spanner integration pattern and the BaseToolset entry point. Use MilvusVectorStore, MilvusToolset, MilvusToolSettings, and MilvusVectorStoreSettings as the scope, with pymilvus>=2.5.0 exposed through google-adk[milvus]. Done means collection setup, ingestion, similarity_search exposure, and user-supplied embeddings are covered.

索引モデルが issue の本文から書いたものです。