Monotonic constraint does not work as expected

I have trained an XGBregressor model with following parameters:

{‘objective’: ‘reg:squarederror’,
‘base_score’: 0.5,
‘booster’: ‘gbtree’,
‘colsample_bylevel’: 1,
‘colsample_bynode’: 1,
‘colsample_bytree’: 1,
‘gamma’: 0,
‘gpu_id’: -1,
‘interaction_constraints’: ‘’,
‘learning_rate’: 0.100000001,
‘max_delta_step’: 0,
‘max_depth’: 10,
‘min_child_weight’: 1,
‘monotone_constraints’: ‘(0,0,0,-1,-1,-1,0,0,0,-1)’,
‘n_jobs’: 64,
‘num_parallel_tree’: 1,
‘random_state’: 154,
‘reg_alpha’: 0,
‘reg_lambda’: 1,
‘scale_pos_weight’: 1,
‘subsample’: 1,
‘tree_method’: ‘exact’,
‘validate_parameters’: 1,
‘verbosity’: None,
‘eta’: 0.1,
‘eval_metric’: ‘rmse’,
‘seed’: 154}

python version==3.7
xgboost version==1.6.1

I have a dataset for price of used cars. I expect that for a fixed car price decreases as usage gets higher. But monotonic constraint does not work as expected, I mean for a fixed feature set by changing a monotone feature (like usage) the result was not monotonic.
Would you please help me, how can I solve this issue?