### 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

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include_upstream=False,
                include_downstream=True,
            )
            result.update(
                p_dag._get_task_instances(
                    task_ids=task_ids,
                    start_date=start_date,
                    end_date=end_date,
                    run_id=None,
                    state=state,
                    include_subdags=include_subdags,
                    include_parentdag=False,
                    include_dependent_dags=include_dependent_dags,
                    as_pk_tuple=True,
                    exclude_task_ids=exclude_task_ids,
                    session=session,
                    dag_bag=dag_bag,
                    recursion_depth=recursion_depth,
                    max_recursion_depth=max_recursion_depth,
                    visited_external_tis=visited_external_tis,
                )
            )
        if include_dependent_dags:
            
            from airflow.sensors.external_task import ExternalTaskMarker
            query = tis
            if as_pk_tuple:
                condition = TI.filter_for_tis(TaskInstanceKey(*cols) for cols in tis.all())
                if condition is not None:
                    query = session.query(TI).filter(condition)