### Apache Airflow version

2.6.1

### What happened

A number of Airflow CLI commands will fail with an error like the one below if a DAG uses a [custom Timetable](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/timetable.html): 
```
Traceback (most recent call last):
  File "/opt/conda/envs/production-airflow-2.6.1/bin/airflow", line 11, in <module>
    sys.exit(main())
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/__main__.py", line 48, in main
    args.func(args)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/cli/cli_config.py", line 51, in command
    return func(*args, **kwargs)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/utils/cli.py", line 112, in wrapper
    return f(*args, **kwargs)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py", line 139, in dag_backfill
    _run_dag_backfill(dags, args)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py", line 79, in _run_dag_backfill
    ti.dry_run()
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1730, in dry_run
    self.render_templates()
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 2172, in render_templates
    context = self.get_template_context()
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1931, in get_template_context
    data_interval = dag.get_run_data_interval(dag_run)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/models/dag.py", line 861, in get_run_data_interval
    return self.infer_automated_data_interval(run.execution_date)
  File "/opt/conda/envs/production-airflow-2.6.1/lib/python3.9/site-packages/airflow/models/dag.py", line 882, in infer_automated_data_interval
    raise ValueError(f"Not a valid timetable: {self.timetable!r}")
ValueError: Not a valid timetable: <workday.AfterWorkdayTimetable object at 0x7f50f9cd1700>
```

Some commands I have found that demonstrate this issue (though there may be more that I haven't found):
* `airflow dags backfill <dag_id> -s <date> -e <date> --dry-run` (works fine without `--dry-run`)
* `airflow dags test <dag_id> [date]`
* `airflow tasks render <dag_id> <task_id> <date>` (works fine if you pass a valid `run_id` instead of an `execution_date`)

### What you think should happen instead

Custom Timetables should work in all contexts that built-in timetables do.

### How to reproduce

Set up Airflow in a such a way that example plugins can be used (see comments below).  Add this DAG script:
```
#!/usr/bin/env python
from datetime import datetime

from airflow.decorators import dag, task

# copy airflow/example_dags/plugins/workday.py into your $PLUGINS_FOLDER
# OR update airflow.cfg to set core.plugins_folder = /path/to/airflow/example_dags/plugins
from workday import AfterWorkdayTimetable


@dag(
    start_date=datetime(2023, 5, 13),
    timetable=AfterWorkdayTimetable(),
    catchup=True,
)
def test_timetable():
    @task()
    def t():
        print('hello')

    t()


test_timetable()
```
To verify that the timetable is working correctly, you should be able to unpause the DAG and see tasks complete.  Then run one of the CLI commands that I mentioned above to see the error.

### Operating System

CentOS Stream 8

### Versions of Apache Airflow Providers

N/A

### Deployment

Other

### Deployment details

Self-hosted deployment/`standalone` mode
Postgres DB backend

### Anything else

Possibly related:
* #19578

### 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)

from airflow.timetables.interval import _DataIntervalTimetable
        warnings.warn(
            "`DAG.previous_schedule()` is deprecated.",
            category=RemovedInAirflow3Warning,
            stacklevel=2,
        )
        if not isinstance(self.timetable, _DataIntervalTimetable):
            return None
        return self.timetable._get_prev(timezone.coerce_datetime(dttm))
    def get_next_data_interval(self, dag_model: DagModel) -> DataInterval | None:
        """Get the data interval of the next scheduled run.
        For compatibility, this method infers the data interval from the DAG's
        schedule if the run does not have an explicit one set, which is possible
        for runs created prior to AIP-39.
        This function is private to Airflow core and should not be depended on as a
        part of the Python API.
        :meta private:
        """
        if self.dag_id != dag_model.dag_id:
            raise ValueError(f"Arguments refer to different DAGs: {self.dag_id} != {dag_model.dag_id}")
        if dag_model.next_dagrun is None:  
            return None
        data_interval = dag_model.next_dagrun_data_interval
        if data_interval is not None:
            return data_interval
        
        
        
        return self.infer_automated_data_interval(dag_model.next_dagrun)
    def get_run_data_interval(self, run: DagRun) -> DataInterval: