Rename the `ChatMessage` and `AgentEvent` base classes to `BaseChatMessage` and `BaseAgentEvent`. 

Bring back the `ChatMessage` and `AgentEvent` as union of built-in concrete types to avoid breaking existing applications that depends on Pydantic serialization. 

Why?

Many existing code uses containers like this:

```python
class AppMessage(BaseModel):
   name: str
   message: ChatMessage 

# Serialization is this:
m = AppMessage(...)
m.model_dump_json()

# Fields like HandoffMessage.target will be lost because it is now treated as a base class without content or target fields.
```

The assumption on `ChatMessage` or `AgentEvent` to be a union of concrete types could be in many existing code bases. So this PR brings back the union types, while keep method type hints such as those on `on_messages` to use the `BaseChatMessage` and `BaseAgentEvent` base classes for flexibility.

This PR refactored `AgentEvent` and `ChatMessage` union types to
abstract base classes. This allows for user-defined message types that
subclass one of the base classes to be used in AgentChat.

To support a unified interface for working with the messages, the base
classes added abstract methods for:
- Convert content to string
- Convert content to a `UserMessage` for model client
- Convert content for rendering in console.
- Dump into a dictionary
- Load and create a new instance from a dictionary

This way, all agents such as `AssistantAgent` and `SocietyOfMindAgent`
can utilize the unified interface to work with any built-in and
user-defined message type.

This PR also introduces a new message type, `StructuredMessage` for
AgentChat (Resolves #5131), which is a generic type that requires a
user-specified content type.

You can create a `StructuredMessage` as follow:

```python

class MessageType(BaseModel):
  data: str
  references: List[str]

message = StructuredMessage[MessageType](content=MessageType(data="data", references=["a", "b"]), source="user")

# message.content is of type `MessageType`. 
```

This PR addresses the receving side of this message type. To produce
this message type from `AssistantAgent`, the work continue in #5934.

Added unit tests to verify this message type works with agents and
teams.

## Summary of Changes
- Added 'candidate_func' to 'SelectorGroupChat' to narrow-down the pool
of candidate speakers.
- Introduced a test in tests/test_group_chat_endpoint.py to validate its
functionality.
- Updated the selector group chat user guide with an example
demonstrating 'candidate_func'.

## Why are these changes needed?
- These changes adds a new parameter `candidate_func` to
`SelectorGroupChat` that helps user narrow-down the set of agents for
speaker selection, allowing users to automatically select next speaker
from a smaller pool of agents.

## Related issue number
Closes #5828

## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.

---------

Signed-off-by: Abhijeetsingh Meena <abhijeet040403@gmail.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Don't throw an exception when model makes a mistake. Use retries, and if
not succeeding after a fixed attempts, fall back to the previous sepaker
if available, or the first participant.

Resolves #5453

Resolves #5322