### 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.
DatasetDagRunQueue(dataset_id=ds1_id, target_dag_id=dag.dag_id, created_at=DEFAULT_DATE),
                DatasetDagRunQueue(
                    dataset_id=ds2_id, target_dag_id=dag.dag_id, created_at=DEFAULT_DATE + timedelta(hours=1)
                ),
            ]
        )
        session.flush()
        query, dataset_triggered_dag_info = DagModel.dags_needing_dagruns(session)
        assert 1 == len(dataset_triggered_dag_info)
        assert dag.dag_id in dataset_triggered_dag_info
        first_queued_time, last_queued_time = dataset_triggered_dag_info[dag.dag_id]
        assert first_queued_time == DEFAULT_DATE
        assert last_queued_time == DEFAULT_DATE + timedelta(hours=1)
class TestQueries:
    def setup_method(self) -> None:
        clear_db_runs()
    def teardown_method(self) -> None:
        clear_db_runs()
    @pytest.mark.parametrize("tasks_count", [3, 12])
    def test_count_number_queries(self, tasks_count):
        dag = DAG("test_dagrun_query_count", start_date=DEFAULT_DATE)
        for i in range(tasks_count):
            EmptyOperator(task_id=f"dummy_task_{i}", owner="test", dag=dag)
        with assert_queries_count(2):
            dag.create_dagrun(
                run_id="test_dagrun_query_count",
                state=State.RUNNING,
                execution_date=TEST_DATE,
            )
class TestDagDecorator: