### Apache Airflow version

2.2.2 (latest released)

### Operating System

Darwin Kernel Version 21.1.0 RELEASE_ARM64_T8101 arm64

### Versions of Apache Airflow Providers

_No response_

### Deployment

Virtualenv installation

### Deployment details

python_version  | 3.9.7 (default, Sep 16 2021, 23:53:23)  [Clang 12.0.0 ]

### What happened

The following error is displayed in Web UI:
```
Broken DAG: [<EDITED>/scripts/airflow/dags/master/sample_dag/sample_dag.py] Traceback (most recent call last):
  File "<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 271, in serialize_to_json
    serialized_object[key] = _encode_timetable(value)
  File "<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 152, in _encode_timetable
    raise _TimetableNotRegistered(importable_string)
airflow.serialization.serialized_objects._TimetableNotRegistered: Timetable class 'workday.AfterWorkdayTimetable' is not registered

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 937, in to_dict
    json_dict = {"__version": cls.SERIALIZER_VERSION, "dag": cls.serialize_dag(var)}
  File "<EDITED>/miniconda3/envs/dev-airflow/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 849, in serialize_dag
    raise SerializationError(f'Failed to serialize DAG {dag.dag_id!r}: {e}')
airflow.exceptions.SerializationError: Failed to serialize DAG 'learning_example_workday_timetable': Timetable class 'workday.AfterWorkdayTimetable' is not registered
```

### What you expected to happen

For the custom timetable to be implemented and used by DAG.

### How to reproduce

Following instructions from [Custom DAG Scheduling with Timetables](https://airflow.apache.org/docs/apache-airflow/stable/howto/timetable.html) with following new DAG to implement:

```python
import datetime

from airflow import DAG
from airflow import plugins_manager
from airflow.operators.dummy import DummyOperator

from workday import AfterWorkdayTimetable

with DAG(
    dag_id="learning_example_workday_timetable",
    start_date=datetime.datetime(2021,11,20),
    timetable=AfterWorkdayTimetable(),
    tags=["example","learning","timetable"],
) as dag:
    DummyOperator(task_id="run_this")
```

### Anything else

I have tried digging through the code and believe the issue is in this line:
https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L149

Perhaps the [Custom DAG Scheduling with Timetables](https://airflow.apache.org/docs/apache-airflow/stable/howto/timetable.html) expects an `__eq__` implemented in the `AfterWorkdayTimetable` class but it would appear that the `AfterWorkdayTimetable` class imported through the DAG and the `AfterWorkdayTimetable` class imported through `plugin_manager` have different `id()`'s:
https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L129

The only way I could get it to import successfully was via the following sequence of import statements since [_get_registered_timetable](https://github.com/apache/airflow/blob/fb478c00cdc5e78d5e85fe5ac103707c829be2fb/airflow/serialization/serialized_objects.py#L124) uses a lazy import:
```python
import datetime

from airflow import DAG
from airflow import plugins_manager
from airflow.operators.dummy import DummyOperator

plugins_manager.initialize_timetables_plugins()
from workday import AfterWorkdayTimetable
```

I also had the webserver and scheduler restarted and confirmed the plugin is seen via cli:
```bash
airflow plugins                                                                                                                  ✔  dev-airflow   19:01:43 
name                     | source
=========================+===========================
workday_timetable_plugin | $PLUGINS_FOLDER/workday.py
```

### Are you willing to submit PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

airflow/serialization/serialized_objects.py
"""
    A JSON serializable representation of DAG.
    A stringified DAG can only be used in the scope of scheduler and webserver, because fields
    that are not serializable, such as functions and customer defined classes, are casted to
    strings.
    Compared with SimpleDAG: SerializedDAG contains all information for webserver.
    Compared with DagPickle: DagPickle contains all information for worker, but some DAGs are
    not pickle-able. SerializedDAG works for all DAGs.
    """
    _decorated_fields = {'schedule_interval', 'default_args', '_access_control'}
    @staticmethod
    def __get_constructor_defaults():
        param_to_attr = {
            'max_active_tasks': '_max_active_tasks',
            'description': '_description',
            'default_view': '_default_view',
            'access_control': '_access_control',
        }
        return {
            param_to_attr.get(k, k): v.default
            for k, v in signature(DAG.__init__).parameters.items()
            if v.default is not v.empty
        }
    _CONSTRUCTOR_PARAMS = __get_constructor_defaults.__func__()  
    del __get_constructor_defaults
    _json_schema = load_dag_schema()
    @classmethod
    def serialize_dag(cls, dag: DAG) -> dict: