### Description

Currently Airflow has a `Filter Upstream` View/option inside the graph view. (As documented [here](https://docs.astronomer.io/learn/airflow-ui#graph-view) under `Filter Upstream`)

<img width="682" alt="image" src="https://user-images.githubusercontent.com/9246654/211711759-670a1180-7f90-4ecd-84b0-2f3b290ff477.png">

It would be great if there were also the options
1. `Filter Downstream` &
2. `Filter Downstream & Upstream`

### Use case/motivation

Sometimes it is useful to view downstream tasks & down & upstream tasks when reviewing dags. This feature would make it as easy to view those as it is to view upstream today.

### Related issues

I found nothing with a quick search

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

func.sum(case((DatasetEvent.id.is_not(None), 1), else_=0)).label("total_updates"),
                )
                .join(DatasetEvent, DatasetEvent.dataset_id == DatasetModel.id, isouter=not has_event_filters)
                .group_by(
                    DatasetModel.id,
                    DatasetModel.uri,
                )
                .order_by(*order_by)
            )
            if has_event_filters:
                count_query = count_query.join(DatasetEvent, DatasetEvent.dataset_id == DatasetModel.id)
            filters = [~DatasetModel.is_orphaned]
            if uri_pattern:
                filters.append(DatasetModel.uri.ilike(f"%{uri_pattern}%"))
            if updated_after:
                filters.append(DatasetEvent.timestamp >= updated_after)
            if updated_before:
                filters.append(DatasetEvent.timestamp <= updated_before)
            query = query.filter(*filters)
            count_query = count_query.filter(*filters)
            query = query.offset(offset).limit(limit)
            datasets = [dict(dataset) for dataset in query.all()]
            data = {"datasets": datasets, "total_entries": count_query.scalar()}
            return (
                htmlsafe_json_dumps(data, separators=(",", ":"), cls=utils_json.WebEncoder),
                {"Content-Type": "application/json; charset=utf-8"},
            )
    @expose("/robots.txt")
    @action_logging
    def robots(self):