Can I consider this pattern of data as a linear and use parametric multiple linear regression?
I don't think "can" is the right question to ask; it's not going to give you a syntax error. The right question is "what could go wrong?". Any modeling technique will have assumptions that may be broken, and knowing how those assumptions impact the results will help you know what to look for (and how much to care when those assumptions are broken).


The best test of whether or not linearity is appropriate is whether the residuals are white or structured. For example, it looks like X9 might have a nonlinear relationship with Y. But that might be an artifact of the interaction between X9 and other variables, especially categorical variables. Fit your full model, then plot the residuals against X9 and see what it looks like.
Treating it as continuous won't cause serious problems, but you might want to think about what this implies. Is the relationship between 1 and 2 in the same direction and half the strength as the relationship between 2 and 4? If not, you might want to transform this to a scale where you do think the differences are linear.
Same as 2, except it's even more reasonable to see time as linear.
Standardization is not necessary for most linear regression techniques, as they contain their own standardization. The primary exception is techniques that use regularization, where the scale of the parameters is relevant.


It's also worth pointing out that multivariate linear relationships, while they can capture general trends well, are very poor at capturing logical trends. For example, looking at X3 and X4, it could very well be that there are rules like Y>X3 and Y>X4 in place, which is hinted at but not captured by linear regression.