Rewrite approx (#7214)

This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.

The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.

demo/guide-python/categorical.py

@@ -3,7 +3,8 @@ Getting started with categorical data
 =====================================
 
 Experimental support for categorical data.  After 1.5 XGBoost `gpu_hist` tree method has
-experimental support for one-hot encoding based tree split.
+experimental support for one-hot encoding based tree split, and in 1.6 `approx` supported
+was added.
 
 In before, users need to run an encoder themselves before passing the data into XGBoost,
 which creates a sparse matrix and potentially increase memory usage.  This demo showcases