### 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.
input_datasets: dict[Dataset, None] = {}
        
        
        
        
        for dag in dags:
            curr_orm_dag = existing_dags.get(dag.dag_id)
            if not dag.dataset_triggers:
                if curr_orm_dag and curr_orm_dag.schedule_dataset_references:
                    curr_orm_dag.schedule_dataset_references = []
            for dataset in dag.dataset_triggers:
                dag_references[dag.dag_id].add(dataset.uri)
                input_datasets[DatasetModel.from_public(dataset)] = None
            curr_outlet_references = curr_orm_dag and curr_orm_dag.task_outlet_dataset_references
            for task in dag.tasks:
                dataset_outlets = [x for x in task.outlets or [] if isinstance(x, Dataset)]
                if not dataset_outlets:
                    if curr_outlet_references:
                        this_task_outlet_refs = [
                            x
                            for x in curr_outlet_references
                            if x.dag_id == dag.dag_id and x.task_id == task.task_id
                        ]
                        for ref in this_task_outlet_refs:
                            curr_outlet_references.remove(ref)
                for d in dataset_outlets:
                    outlet_references[(task.dag_id, task.task_id)].add(d.uri)
                    outlet_datasets[DatasetModel.from_public(d)] = None
        all_datasets = outlet_datasets
        all_datasets.update(input_datasets)