### Body

There are some use cases where users want to trigger new DAG run as soon as one finished. This is a request I've seen several times with some variations (for example like this [Stackoverflow question](https://stackoverflow.com/q/75623153/14624409)) but the basic request is the same.

The workaround users do to get such functionality is place `TriggerDagRunOperator` as last task of their DAG invoking the same DAG:

```
from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule=None,
) as dag:
    task = EmptyOperator(task_id="first")
    trigger = TriggerDagRunOperator(
        task_id="trigger",
        trigger_dag_id="example",
    )

    task >> trigger

```

As you can see this works nicely:
![Screenshot 2023-03-03 at 14 20 51](https://user-images.githubusercontent.com/45845474/222718862-dfde8a41-24b6-4991-b318-b7f9784514f6.png)

My suggestion is to add first class support for this use case, so the above example will be changed to:

```

from datetime import datetime

from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator

with DAG(
    dag_id="example",
    start_date=datetime(2023, 1, 1,),
    catchup=False,
    schedule="@continues",
) as dag:
    task = EmptyOperator(task_id="first")
```

I guess it won't exactly be `"@continues"` but more likely new [ScheduleArg](https://github.com/apache/airflow/blob/8b8552f5c4111fe0732067d7af06aa5285498a79/airflow/models/dag.py#L127) type but I show it like that just for simplification of the idea.

### Committer

- [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
def next_dagrun_info(self, last_automated_data_interval, restriction):
                raise RuntimeError("this fails")
        dag = DAG(
            "test_next_dagrun_info_timetable_exception",
            start_date=timezone.datetime(2020, 5, 1),
            timetable=FailingTimetable(),
            catchup=True,
        )
        def _check_logs(records: list[logging.LogRecord], data_interval: DataInterval) -> None:
            assert len(records) == 1
            record = records[0]
            assert record.exc_info is not None, "Should contain exception"
            assert record.getMessage() == (
                f"Failed to fetch run info after data interval {data_interval} "
                f"for DAG 'test_next_dagrun_info_timetable_exception'"
            )
        with caplog.at_level(level=logging.ERROR):
            next_info = dag.next_dagrun_info(None)
        assert next_info is None, "failed next_dagrun_info should return None"
        _check_logs(caplog.records, data_interval=None)
        caplog.clear()
        data_interval = DataInterval(timezone.datetime(2020, 5, 1), timezone.datetime(2020, 5, 2))
        with caplog.at_level(level=logging.ERROR):
            next_info = dag.next_dagrun_info(data_interval)
        assert next_info is None, "failed next_dagrun_info should return None"
        _check_logs(caplog.records, data_interval)
    def test_next_dagrun_after_auto_align(self):