### Apache Airflow version

2.2.3 (latest released)

### What happened

When issuing the following command:

`airflow dags trigger 'VMWARE_BACKUP'  --conf '{"VM_NAME":"psfiplb1"}'`

the system replays with:

```
/usr/local/lib/python3.8/dist-packages/airflow/api/common/experimental/trigger_dag.py:85 DeprecationWarning: Calling `DAG.create_dagrun()` without an explicit data interval is deprecated
Created <DagRun VMWARE_BACKUP @ 2021-12-31T00:00:00+00:00: manual__2021-12-31T00:00:00+00:00, externally triggered: True>
```

### What you expected to happen

I have no other switch parameter to choose for avoiding the deprecation warning.

My concern is about what will happen to the command when the deprecation will transforms itself in an error.

### How to reproduce

_No response_

### Operating System

Ubuntu 20.04.3 LTS

### Versions of Apache Airflow Providers

apache-airflow-providers-celery==2.1.0
apache-airflow-providers-ftp==2.0.1
apache-airflow-providers-http==2.0.1
apache-airflow-providers-imap==2.0.1
apache-airflow-providers-microsoft-mssql==2.0.1
apache-airflow-providers-microsoft-winrm==2.0.1
apache-airflow-providers-openfaas==2.0.0
apache-airflow-providers-oracle==2.0.1
apache-airflow-providers-samba==3.0.1
apache-airflow-providers-sftp==2.3.0
apache-airflow-providers-sqlite==2.0.1
apache-airflow-providers-ssh==2.3.0

### Deployment

Virtualenv installation

### Deployment details

Airflow inside an LXD continer

### 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)

"""
    Get task instance dependencies that were not met.
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow tasks failed-deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    """
    dag = get_dag(args.subdir, args.dag_id)
    task = dag.get_task(task_id=args.task_id)
    ti, _ = _get_ti(task, args.map_index, exec_date_or_run_id=args.execution_date_or_run_id)
    dep_context = DepContext(deps=SCHEDULER_QUEUED_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    
    if failed_deps:
        print("Task instance dependencies not met:")
        for dep in failed_deps:
            print(f"{dep.dep_name}: {dep.reason}")
    else:
        print("Task instance dependencies are all met.")
@cli_utils.action_cli(check_db=False)
@suppress_logs_and_warning
def task_state(args):