feat: unify max_infer_iters in client/server agent loop (#1309)

# What does this PR do?

We currently use `max_infer_iters` in 2 different ways
1/ Server: track number of times 
2/ Client side: track number of times we send `resume_turn` request

This PR gets rid of the need of (2) and makes server track total number
of times we perform inference within a Turn

**NOTE**
The PR will assume StopReason is set to
- end_of_message: turn is not finished, we could be waiting for client
tool call responses
- end_of_turn: if the entire turn is finished and there's no more things
to be done.

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
```
LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v tests/client-sdk/agents/test_agents.py::test_custom_tool_infinite_loop --inference-model "meta-llama/Llama-3.3-70B-Instruct"
```

[//]: # (## Documentation)

tests/client-sdk/agents/test_agents.py

@@ -278,6 +278,33 @@ def test_custom_tool(llama_stack_client, agent_config):
     assert "get_boiling_point" in logs_str
 
 
+def test_custom_tool_infinite_loop(llama_stack_client, agent_config):
+    client_tool = get_boiling_point
+    agent_config = {
+        **agent_config,
+        "instructions": "You are a helpful assistant Always respond with tool calls no matter what. ",
+        "client_tools": [client_tool.get_tool_definition()],
+        "max_infer_iters": 5,
+    }
+
+    agent = Agent(llama_stack_client, agent_config, client_tools=(client_tool,))
+    session_id = agent.create_session(f"test-session-{uuid4()}")
+
+    response = agent.create_turn(
+        messages=[
+            {
+                "role": "user",
+                "content": "Get the boiling point of polyjuice with a tool call.",
+            },
+        ],
+        session_id=session_id,
+        stream=False,
+    )
+
+    num_tool_calls = sum([1 if step.step_type == "tool_execution" else 0 for step in response.steps])
+    assert num_tool_calls <= 5
+
+
 def test_tool_choice(llama_stack_client, agent_config):
     def run_agent(tool_choice):
         client_tool = get_boiling_point