### 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.
"""
        Triggers the appropriate callback depending on the value of success, namely the
        on_failure_callback or on_success_callback. This method gets the context of a
        single TaskInstance part of this DagRun and passes that to the callable along
        with a 'reason', primarily to differentiate DagRun failures.
        .. note: The logs end up in
            ``$AIRFLOW_HOME/logs/scheduler/latest/PROJECT/DAG_FILE.py.log``
        :param dagrun: DagRun object
        :param success: Flag to specify if failure or success callback should be called
        :param reason: Completion reason
        :param session: Database session
        """
        callbacks = self.on_success_callback if success else self.on_failure_callback
        if callbacks:
            callbacks = callbacks if isinstance(callbacks, list) else [callbacks]
            tis = dagrun.get_task_instances(session=session)
            ti = tis[-1]  
            ti.task = self.get_task(ti.task_id)
            context = ti.get_template_context(session=session)
            context.update({"reason": reason})
            for callback in callbacks:
                self.log.info("Executing dag callback function: %s", callback)
                try:
                    callback(context)
                except Exception:
                    self.log.exception("failed to invoke dag state update callback")
                    Stats.incr(
                        "dag.callback_exceptions", tags={"dag_id": dagrun.dag_id, "run_id": dagrun.run_id}
                    )
    def get_active_runs(self):