feat: implement Google Batch Mode support for LLM calls

- Add google-generativeai dependency to pyproject.toml
- Extend LLM class with batch mode parameters (batch_mode, batch_size, batch_timeout)
- Implement batch request management methods for Gemini models
- Add batch-specific event types (BatchJobStartedEvent, BatchJobCompletedEvent, BatchJobFailedEvent)
- Create comprehensive test suite for batch mode functionality
- Add example demonstrating batch mode usage with cost savings
- Support inline batch requests for up to 50% cost reduction on Gemini models

Resolves issue #3116

Co-Authored-By: João <joao@crewai.com>

examples/batch_mode_example.py

@@ -0,0 +1,63 @@
+"""
+Example demonstrating Google Batch Mode support in CrewAI.
+
+This example shows how to use batch mode with Gemini models to reduce costs
+by up to 50% for non-urgent LLM calls.
+"""
+
+import os
+from crewai import Agent, Task, Crew
+from crewai.llm import LLM
+
+os.environ["GOOGLE_API_KEY"] = "your-google-api-key-here"
+
+def main():
+    batch_llm = LLM(
+        model="gemini/gemini-1.5-pro",
+        batch_mode=True,
+        batch_size=5,  # Process 5 requests at once
+        batch_timeout=300,  # Wait up to 5 minutes for batch completion
+        temperature=0.7
+    )
+    
+    research_agent = Agent(
+        role="Research Analyst",
+        goal="Analyze market trends and provide insights",
+        backstory="You are an expert market analyst with years of experience.",
+        llm=batch_llm,
+        verbose=True
+    )
+    
+    tasks = []
+    topics = [
+        "artificial intelligence market trends",
+        "renewable energy investment opportunities", 
+        "cryptocurrency regulatory landscape",
+        "e-commerce growth projections",
+        "healthcare technology innovations"
+    ]
+    
+    for topic in topics:
+        task = Task(
+            description=f"Research and analyze {topic}. Provide a brief summary of key trends and insights.",
+            agent=research_agent,
+            expected_output="A concise analysis with key findings and trends"
+        )
+        tasks.append(task)
+    
+    crew = Crew(
+        agents=[research_agent],
+        tasks=tasks,
+        verbose=True
+    )
+    
+    print("Starting batch processing...")
+    print("Note: Batch requests will be queued until batch_size is reached")
+    
+    result = crew.kickoff()
+    
+    print("Batch processing completed!")
+    print("Results:", result)
+
+if __name__ == "__main__":
+    main()