### 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.
get_ti_from_db(task_1).state = State.FAILED
    get_ti_from_db(task_2).state = State.SUCCESS
    get_ti_from_db(task_3).state = State.UPSTREAM_FAILED
    get_ti_from_db(task_4).state = State.FAILED
    get_ti_from_db(task_5).state = State.SKIPPED
    session.flush()
    altered = dag.set_task_instance_state(
        task_id=task_1.task_id,
        run_id=run_id,
        execution_date=execution_date,
        state=State.SUCCESS,
        session=session,
    )
    
    ti1 = get_ti_from_db(task_1)
    assert ti1.state == State.SUCCESS
    
    assert isinstance(inspect(ti1).attrs.dag_run.loaded_value, DagRun)
    
    assert get_ti_from_db(task_2).state == State.SUCCESS
    
    assert get_ti_from_db(task_3).state == State.NONE
    assert get_ti_from_db(task_4).state == State.NONE
    
    assert get_ti_from_db(task_5).state == State.SKIPPED
    dagrun.refresh_from_db(session=session)
    
    assert dagrun.get_state() == State.QUEUED
    assert {t.key for t in altered} == {("test_set_task_instance_state", "task_1", dagrun.run_id, 1, -1)}
def test_set_task_instance_state_mapped(dag_maker, session):