First, dimensionality is not a single concept that has a well defined meaning in psychometrics. Dimensionality as assessed by principle component analysis (PCA) is different than dimensionality assessed by item response theory (IRT). Below is a citation an old review highlighting the ambiguity of the term "unidimensional." Though it is old, I still see papers where the authors seems unaware of some of the issues raised in it.

Reference:

Hattie, John. "Methodology review: assessing unidimensionality of tests and ltenls." Applied Psychological Measurement 9.2 (1985): 139-164.

To move more specifically to your question:

PCA is a variance-based method that seeks to explain the total observed variance in a set of responses. A similar technique is factor analysis, which seeks to explain the common variance. IRT is not concerned with explaining variance but with ordering items along a latent trait, something that variance-based methods have no regard for.

That being said, variance-based approaches and IRT-based approaches often (though far from always) suggest the same number of dimensions.

Another point worth mentioning here is that IRT is a method that is designed for ordinal and categorical data, while PCA is designed for continuous variables. Frequently, ordinal data is treated as continuous and used in PCA, which is not necessarily a problem but can be responsible for quirks in the data. One special way to use variance-based methods with ordinal data is to use polychoric or tetrachoric correlations, which explicitly assume that the data is ordinal but represents thresholds along a continuous response. Polychoric correlations are sometimes different than pearson correlations, and as such statistical techniques looking at explaining variance (on which correlations are based) will give different answers if the polychoric correlations are different. Notably, the technique used to find polychoric correlations might make some different assumptions that your data violates.

Though you did not ask, I will point out that you can use polychoric correlations to do factor analysis and from the results of the factor analysis and the polychoric correlation thresholds, you can recover the IRT parameters - whether unidimensional or multidimensional. The mathematical relationships between IRT parameters and factor analysis has been explored in the reference below:

Kamata, Akihito, and Daniel J. Bauer. "A note on the relation between factor analytic and item response theory models." Structural Equation Modeling 15.1 (2008): 136-153.

This can be done even in multiple dimensions. Notably, if you have binary items, the factor-analytic estimates of IRT parameters will essentially be Birnbaum's 2 parameter model. If you have ordinal / polytomous items, then the factor analytic IRT estimates will be estimates of a graded response model (as opposed to a generalized partial credit model).

In summary:

Is your approach of using PCA followed by IRT reasonable? Sure, but if two different solutions for the number of dimensions are found then you have to ask the question what is that you mean by the term "dimension?" Do you mean explained variance or the ability locate items in terms of difficulty along a latent trait? If what you are interested in is the latter, then it is very reasonable to take the results of the IRT models over the PCA-based models.