GridSearch returns worse results than default configuration
Technically: Because grid search creates subsamples of the data repeatedly. That means the SVC is trained on 80% of x_train in each iteration and the results are the mean of predictions on the other 20%.  

Theoretically: Because you conflate the questions of hyperparameter tuning (selection) and model performance estimation. GridSearch is used for selecting a combination of hyperparameters, performance estimation has not yet happened. The only comparison you should be making is between the parameter combinations within the CV itself (grid_results.cv_results). In my opinion, the reported CV train accuracy is within acceptable boundaries from non-CV training (meaning your SVC is able to extract a lot of generalization from subsamples). See e.g Cawley 2010

It would be interesting to see the reported performance on x_test on the retrained estimator grid_results.best_estimator_.predict(x_test), if it was kept separate from x_train, and how it is different from the first results.