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.

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressorSuite.scala

@@ -53,7 +53,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
       "max_depth" -> "6",
       "silent" -> "1",
       "objective" -> "reg:squarederror",
-      "max_bin" -> 16,
+      "max_bin" -> 64,
       "tree_method" -> treeMethod)
 
     val model1 = ScalaXGBoost.train(trainingDM, paramMap, round)