Does Zero Observations Problem exist for Gaussian Naive Bayes?

I'm currently implementing a Gaussian Naive Bayes classifier. With a Naive Bayes classifier the zero observation or zero probability problem can occur, see e.g. point 11 on http://machinelearningmastery.com/better-naive-bayes/. This can be solved by e.g. laplacian smoothing.

Am I right that this problem cannot happen for Gaussian Naive Bayes? Because there we use the normal distribution for calculating the probabilities (using the mean and standard deviation calculated for each feature).

Now, how does this relate to continuous variables? Recall that in continuous case $P(X = x) = 0$, so correcting for missing point-values does not make sens. In this case, "zero probability" problem would be that (a) you know that your data is censored, truncated, has outliers etc., and you want to make corrections in your model for that fact, or (b) you, in general, want to include out-of-sample knowledge (prior) in your model (a fully Bayesian model).