### 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/jobs/backfill_job.py
run.update_state(session=session)
                if run.state in State.finished:
                    ti_status.finished_runs += 1
                    ti_status.active_runs.remove(run)
                    executed_run_dates.append(run.execution_date)
            self._log_progress(ti_status)
        
        return executed_run_dates
    @provide_session
    def _collect_errors(self, ti_status, session=None):
        def tabulate_ti_keys_set(set_ti_keys: Set[TaskInstanceKey]) -> str:
            
            sorted_ti_keys = sorted(
                set_ti_keys,
                key=lambda ti_key: (ti_key.run_id, ti_key.dag_id, ti_key.task_id, ti_key.try_number),
            )
            return tabulate(sorted_ti_keys, headers=["DAG ID", "Task ID", "Run ID", "Try number"])
        def tabulate_tis_set(set_tis: Set[TaskInstance]) -> str:
            
            sorted_tis = sorted(set_tis, key=lambda ti: (ti.run_id, ti.dag_id, ti.task_id, ti.try_number))
            tis_values = ((ti.dag_id, ti.task_id, ti.run_id, ti.try_number) for ti in sorted_tis)
            return tabulate(tis_values, headers=["DAG ID", "Task ID", "Run ID", "Try number"])
        err = ''
        if ti_status.failed:
            err += "Some task instances failed:\n"
            err += tabulate_ti_keys_set(ti_status.failed)
        if ti_status.deadlocked:
            err += 'BackfillJob is deadlocked.'
            deadlocked_depends_on_past = any(
                t.are_dependencies_met(