### 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)

are merged into the new *schedule* argument.
    :param dag_id: The id of the DAG; must consist exclusively of alphanumeric
        characters, dashes, dots and underscores (all ASCII)
    :param description: The description for the DAG to e.g. be shown on the webserver
    :param schedule: Defines the rules according to which DAG runs are scheduled. Can
        accept cron string, timedelta object, Timetable, or list of Dataset objects.
        See also :doc:`/howto/timetable`.
    :param start_date: The timestamp from which the scheduler will
        attempt to backfill
    :param end_date: A date beyond which your DAG won't run, leave to None
        for open-ended scheduling
    :param template_searchpath: This list of folders (non-relative)
        defines where jinja will look for your templates. Order matters.
        Note that jinja/airflow includes the path of your DAG file by
        default
    :param template_undefined: Template undefined type.
    :param user_defined_macros: a dictionary of macros that will be exposed
        in your jinja templates. For example, passing ``dict(foo='bar')``
        to this argument allows you to ``{{ foo }}`` in all jinja
        templates related to this DAG. Note that you can pass any
        type of object here.
    :param user_defined_filters: a dictionary of filters that will be exposed
        in your jinja templates. For example, passing
        ``dict(hello=lambda name: 'Hello %s' % name)`` to this argument allows
        you to ``{{ 'world' | hello }}`` in all jinja templates related to
        this DAG.
    :param default_args: A dictionary of default parameters to be used
        as constructor keyword parameters when initialising operators.
        Note that operators have the same hook, and precede those defined
        here, meaning that if your dict contains `'depends_on_past': True`