### 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):
            if last_automated_data_interval is None:
                return DagRunInfo.interval(start, end)
            raise RuntimeError("this fails")
    dag = DAG(
        dag_id="test_iter_dagrun_infos_between_error",
        start_date=DEFAULT_DATE,
        timetable=FailingAfterOneTimetable(),
    )
    iterator = dag.iter_dagrun_infos_between(earliest=start, latest=end, align=True)
    with caplog.at_level(logging.ERROR):
        infos = list(iterator)
    
    assert infos == [DagRunInfo.interval(start, end)]
    assert caplog.record_tuples == [
        (
            "airflow.models.dag.DAG",
            logging.ERROR,
            f"Failed to fetch run info after data interval {DataInterval(start, end)} for DAG {dag.dag_id!r}",
        ),
    ]
    assert caplog.records[0].exc_info is not None, "should contain exception context"
@pytest.mark.parametrize(
    "logical_date, data_interval_start, data_interval_end, expected_data_interval",
    [
        pytest.param(None, None, None, None, id="no-next-run"),
        pytest.param(
            DEFAULT_DATE,
            DEFAULT_DATE,
            DEFAULT_DATE + timedelta(days=2),