### 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.
"""Serialize the timetable for JSON encoding.
        This is called during DAG serialization to store timetable information
        in the database. This should return a JSON-serializable dict that will
        be fed into ``deserialize`` when the DAG is deserialized. The default
        implementation returns an empty dict.
        """
        return {}
    def validate(self) -> None:
        """Validate the timetable is correctly specified.
        Override this method to provide run-time validation raised when a DAG
        is put into a dagbag. The default implementation does nothing.
        :raises: AirflowTimetableInvalid on validation failure.
        """
        return
    @property
    def summary(self) -> str:
        """A short summary for the timetable.
        This is used to display the timetable in the web UI. A cron expression
        timetable, for example, can use this to display the expression. The
        default implementation returns the timetable's type name.
        """
        return type(self).__name__
    def infer_manual_data_interval(self, *, run_after: DateTime) -> DataInterval:
        """When a DAG run is manually triggered, infer a data interval for it.
        This is used for e.g. manually-triggered runs, where ``run_after`` would
        be when the user triggers the run. The default implementation raises
        ``NotImplementedError``.
        """
        raise NotImplementedError()
    def next_dagrun_info(