Time Complexity $\Theta$ vs. $\Omega$
There are two possible interpretations for "running time of an algorithm". The first interpretation is the worst-case running time, depending on some parameters such as the input length $n$. The second interpretation is that the running time is some bound that holds for all inputs with the given parameters. When we give a big $O$ estimate, both interpretations amount to the same thing: if an algorithm has a running time of $O(n^2)$, then this is both a worst-case bound and a bound which is valid for all inputs of length $n$.

What is then the interpretation of $\Theta(n^2)$ in your case? It's hard to say without more details, and could depend on the context. If it's a worst-case bound then the meaning is "there is a family of inputs for which the algorithm runs in $\Omega(n^2)$ time". If it's a global bound then the meaning is "the algorithm runs in $\Omega(n^2)$ time on all inputs". Out of these two possible interpretations, the first seems slightly more likely, but others may hold different opinions.