You have two issues but both are not a problem of multicollinearity.
Converting a categorical variable to dummy variables
For instance, SIZE1-3 combined is the same as ITEM1 which is the same as A. To avoid this problem
This is not a problem. Your size categories variable is 'nested' within the item category variable. Categorical variables with $n$ categories always translate into $n-1$ dummy variables. (If you have an intercept in the column space of the design-matrix/model)
There are several questions about this dummy-encoding of categorical variables. Here is one with the design-matrix written out completely: Why there is a dependence between the factors on the same column?
Not enough observations
A dependent variable, log price, is obviously affected by ITEM and SIZE. So I want to control these variables, but there is a multicollinearity issue.
In this example you do not only have a situation where size is nested inside item. Another issue is that for several sizes you only have a single observation. If you would control for item/size then the degrees of freedom in the model will be severely reduced.
This multicollinearity doesn't arrise because the variables are correlated in the population, but because you do not have enough observations (making them correlated in the sample).