### 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.
"""Infer a data interval for a run against this DAG.
        This method is used to bridge runs created prior to AIP-39
        implementation, which do not have an explicit data interval. Therefore,
        this method only considers ``schedule_interval`` values valid prior to
        Airflow 2.2.
        DO NOT use this method is there is a known data interval.
        """
        timetable_type = type(self.timetable)
        if issubclass(timetable_type, (NullTimetable, OnceTimetable)):
            return DataInterval.exact(timezone.coerce_datetime(logical_date))
        start = timezone.coerce_datetime(logical_date)
        if issubclass(timetable_type, CronDataIntervalTimetable):
            end = cast(CronDataIntervalTimetable, self.timetable)._get_next(start)
        elif issubclass(timetable_type, DeltaDataIntervalTimetable):
            end = cast(DeltaDataIntervalTimetable, self.timetable)._get_next(start)
        else:
            raise ValueError(f"Not a valid timetable: {self.timetable!r}")
        return DataInterval(start, end)
    def next_dagrun_info(