New feature? Java binding for categorical feature support

We are using XGBoost using the Java binding (outside of Spark) and we have a strong appetite for categorical feature support, where splits are considered in terms of subset partitioning of the categorical feature as opposed to one-hot encoding and having XGboost considering each category separately. The release notes for v1.6 states:

"In the future, we will continue to improve categorical data support with new features and
optimizations. **Also, we are looking forward to bringing the feature beyond Python binding,
contributions and feedback are welcomed!** Lastly, as a result of experimental status, the
behavior might be subject to change, especially the default value of related
hyper-parameters."

I'm raising this issue because I'm wondering what the status is of the Java binding for the experimental parameters related to categorical features. Concretely:

- Is there already a way to communicate to the native C code which columns in the DMatrix should be considered as categorical, and which as numeric?
- Provided that we have some way to encode the feature type in the DMatrix or elsewhere, how do we communicate that to the C binding (there has to be some way to achieve this, since the Python binding already exists)
- Is there an appetite at XGboost maintainers to release such a Java binding in a stable version any time in the next 3-6 months say, provided we contribute a PR that satisfies the general requirements for a XGboost PR?

It seems that some work has already been done on the first two items in (https://github.com/dmlc/xgboost/pull/7966), so perhaps the more general question is:

- Which components are still required to start using categorical features (based on subset partitioning) in Java?
- How can we help get this feature into XGboost faster (e.g., by contributing), provided that it is on the roadmap (https://github.com/dmlc/xgboost/issues/7802)?

I see that this feature request is on the roadmap, and we could contribute to help the process move forward.