Implement robust regularization in 'survival:aft' objective (#5473)

* Robust regularization of AFT gradient and hessian

* Fix AFT doc; expose it to tutorial TOC

* Apply robust regularization to uncensored case too

* Revise unit test slightly

* Fix lint

* Update test_survival.py

* Use GradientPairPrecise

* Remove unused variables

demo/aft_survival/aft_survival_demo.py

@@ -51,4 +51,4 @@ print(df)
 print(df[np.isinf(df['Label (upper bound)'])])
 
 # Save trained model
-bst.save_model('aft_model.json')
+bst.save_model('aft_model.json')

demo/aft_survival/aft_survival_demo_with_optuna.py

@@ -75,4 +75,4 @@ print(df)
 print(df[np.isinf(df['Label (upper bound)'])])
 
 # Save trained model
-bst.save_model('aft_best_model.json')
+bst.save_model('aft_best_model.json')