[Subsystem] Implement Agent Database Query Engine (Port of Ivy.Agent.EfQuery)

Problem Statement

The original C# Ivy-Framework features Ivy.Agent.EfQuery, which integrates with Entity Framework Core to allow AI agents to reflect database schemas dynamically, execute SQL queries safely, and populate DataTable components.

Currently, Rusty-Framework lacks an equivalent database reflection and ORM integration layer (such as SQLx or Diesel integration). As a result, AI agents built on rusty cannot inspect database schemas or execute structured database queries out of the box.

Missing Functionality

• Database schema reflection & metadata extraction (tables, columns, data types, primary/foreign keys).
• Parameterized SQL query execution engine tailored for AI agent tool calling.
• Automatic serialization of database query result sets into rusty DataTable model structures.
• Schema context provider for AI agent prompts.

Proposed Architecture & Design

1. Create a database query abstraction (e.g., rusty-db crate or rusty-server::db module) supporting popular engines (PostgreSQL, SQLite, MySQL) via sqlx.
2. Implement schema inspection tools that produce structured JSON metadata for AI agent system prompts.
3. Expose dynamic query execution functions that validate, sanitize, and execute query parameters.
4. Provide integration helpers to bind query outputs directly into rusty::widgets::DataTable.

Acceptance Criteria

• Schema reflection extracts complete metadata (tables, columns, relationships) into structured Rust types.
• Safe execution engine permits parameterized SQL queries with proper error handling.
• Query results convert seamlessly into DataTable components.
• Comprehensive documentation and example usage with AI agent tool calling.

Start by comparing the proposed rusty-db crate or rusty-server::db module with the existing Rusty-Framework structure, then review the rusty::widgets::DataTable model and the referenced Ivy.Agent.EfQuery behavior. Done means schema metadata, parameterized query execution, DataTable conversion, documentation, and an AI agent tool-calling example are all covered.