How to explain MAE/MSE at each node of decision tree for regression in sklearn python?
Can I conclude that the error at my node is 60 +-13 i.e my values in this particular sample split ranges from 60-13 to 60+13.

No you cannot, because the actual error values depend on the data. For example you might have 1 instance with error 41.11 and 9 instances with error 0:
$$MSE=\frac{41.11^2+0^2+...+0^2}{10}=169$$
This example shows that the only guarantee you have for an individual absolute error is that it's lower or equal than $\sqrt{MSE \times n}$.
The MAE (mean absolute error) is easier to interpret: it's literally the mean of the absolute value of the error.