why Gaussian noise is usually used?
As pointed out in other answers, the Central Limit Theorem is one reason why Gaussian noise is so important as a model. One other reason that hasn't been explicitly mentioned and that I would like to point out is the fact that Gaussian noise is completely described by second order statistics, which are relatively easy to measure. E.g., if you have a univariate distribution and if it is Gaussian, then you know everything you can ever know about it by knowing (measuring) its mean (first order) and its variance (second order). There are no higher order statistics, which is good because they are much harder to measure reliably. This fact is of course no justification for using a Gaussian model, but it is a very handy property if we think that a Gaussian model is justified.