### Apache Airflow version

2.2.0b2 (beta snapshot)

### Operating System

ubuntu 20.04

### Versions of Apache Airflow Providers

n/a

### Deployment

Virtualenv installation

### Deployment details

pip install /path/to/airflow/src

### What happened

Given any DAG initialized with: `schedule_interval=None`

Run `airflow dags test mydagname $(date +%Y-%m-%d)` and get an error:

```
INFO - No run dates were found for the given dates and dag interval.
```

This behavior changed in https://github.com/apache/airflow/pull/15397, it used to trigger a backfill dagrun at the given date.

### What you expected to happen

I expected a backfill dagrun with the given date, regardless of whether it fit into the `schedule_interval`.

If AIP-39 made that an unrealistic expectation, then I'd hope for some way to define unscheduled dags which can still be tested from the command line (which, so far as I know, is the fastest way to iterate on a DAG.).

As it is, I keep changing `schedule_interval` back and forth depending on whether I want to iterate via `astro dev start` (which tolerates `None` but does superfluous work if the dag is scheduled) or via `airflow dags test ...` (which doesn't tolerate `None`).

### How to reproduce

Initialize a DAG with: `schedule_interval=None` and run it via `airflow dags test mydagname $(date +%Y-%m-%d)`

### Anything else

_No response_

### 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/cli/commands/dag_command.py
if dag:
        args.dag_id = dag.dag_id
    dagbag = DagBag()
    if args.dag_id is not None and args.dag_id not in dagbag.dags:
        error_message = f"Dag id {args.dag_id} not found"
        raise AirflowException(error_message)
    state = args.state.lower() if args.state else None
    dag_runs = DagRun.find(
        dag_id=args.dag_id,
        state=state,
        no_backfills=args.no_backfill,
        execution_start_date=args.start_date,
        execution_end_date=args.end_date,
    )
    dag_runs.sort(key=lambda x: x.execution_date, reverse=True)
    AirflowConsole().print_as(
        data=dag_runs,
        output=args.output,
        mapper=lambda dr: {
            "dag_id": dr.dag_id,
            "run_id": dr.run_id,
            "state": dr.state,
            "execution_date": dr.execution_date.isoformat(),
            "start_date": dr.start_date.isoformat() if dr.start_date else '',
            "end_date": dr.end_date.isoformat() if dr.end_date else '',
        },
    )
@provide_session
@cli_utils.action_logging
def dag_test(args, session=None):