### 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.
:param conf: Dict containing configuration/parameters to pass to the DAG
        :param creating_job_id: id of the job creating this DagRun
        :param session: database session
        :param dag_hash: Hash of Serialized DAG
        :param data_interval: Data interval of the DagRun
        """
        logical_date = timezone.coerce_datetime(execution_date)
        if data_interval and not isinstance(data_interval, DataInterval):
            data_interval = DataInterval(*map(timezone.coerce_datetime, data_interval))
        if data_interval is None and logical_date is not None:
            warnings.warn(
                "Calling `DAG.create_dagrun()` without an explicit data interval is deprecated",
                RemovedInAirflow3Warning,
                stacklevel=3,
            )
            if run_type == DagRunType.MANUAL:
                data_interval = self.timetable.infer_manual_data_interval(run_after=logical_date)
            else:
                data_interval = self.infer_automated_data_interval(logical_date)
        if run_type is None or isinstance(run_type, DagRunType):
            pass
        elif isinstance(run_type, str):  
            run_type = DagRunType(run_type)
        else:
            raise ValueError(f"`run_type` should be a DagRunType, not {type(run_type)}")
        if run_id:  
            if not isinstance(run_id, str):
                raise ValueError(f"`run_id` should be a str, not {type(run_id)}")
            inferred_run_type = DagRunType.from_run_id(run_id)
            if run_type is None: