Ridge regression model creation using grid-search and cross validation
Is 0.9113458623386644 my ridge regression accuracy(R squred) ? if it is, then what is meaning of 0.909695864130532 value.

These are both R^2 values.
The first score is the cross-validation score on the training set, and the second is your test set score.   The first is perhaps a little biased, since those models are built using hyperparameters selected while using that dataset (but a different cv-split I think).  The second score should be unbiased, and should probably be your reported test score.  (The second score is a bit better than what should be a optimistically-biased score; perhaps just randomly, perhaps because of the train/test split, and perhaps because that model has seen more data.)

I want check whether my regression model building steps correct or not? can some one explain it?

Things look OK to me.
You could save yourself some code and training time; by default GridSearchCV refits a model on the entire training set using the identified hyperparameters, so you don't need to fit in the last code block.  It also has the cv_results_ and best_score_ attributes to provide you with cross-validation scores, but since you've used the them in selecting the optimal hyperparameters, the best score is no longer an unbiased estimator for future performance.
Note too that there's a builtin for tuning the regularization parameter, but it uses a different CV approach and has RMSE as default scorer.