### 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.
"""
        Return (and lock) a list of Dag objects that are due to create a new DagRun.
        This will return a resultset of rows that is row-level-locked with a "SELECT ... FOR UPDATE" query,
        you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
        transaction is committed it will be unlocked.
        """
        from airflow.models.dataset import DagScheduleDatasetReference, DatasetDagRunQueue as DDRQ
        
        dataset_triggered_dag_info = {
            x.dag_id: (x.first_queued_time, x.last_queued_time)
            for x in session.query(
                DagScheduleDatasetReference.dag_id,
                func.max(DDRQ.created_at).label("last_queued_time"),
                func.min(DDRQ.created_at).label("first_queued_time"),
            )
            .join(DagScheduleDatasetReference.queue_records, isouter=True)
            .group_by(DagScheduleDatasetReference.dag_id)
            .having(func.count() == func.sum(case((DDRQ.target_dag_id.is_not(None), 1), else_=0)))
            .all()
        }
        dataset_triggered_dag_ids = set(dataset_triggered_dag_info.keys())
        if dataset_triggered_dag_ids:
            exclusion_list = {
                x.dag_id
                for x in (
                    session.query(DagModel.dag_id)
                    .join(DagRun.dag_model)
                    .filter(DagRun.state.in_((DagRunState.QUEUED, DagRunState.RUNNING)))
                    .filter(DagModel.dag_id.in_(dataset_triggered_dag_ids))