Understanding Type I and II Errors in Hypothesis Testing

In hypothesis testing, a Type I error occurs when a true null hypothesis is rejected. Conversely, a Type II error occurs when a false null hypothesis is not rejected. Consider the following scenario:

Researchers conducted a study to test whether a new drug was effective in reducing blood pressure compared to a placebo. They set up the null hypothesis ($H_0$): the drug has no effect on blood pressure, and the alternative hypothesis ($H_a$): the drug does have an effect. After conducting the statistical test, the researchers concluded that the drug is effective, leading them to reject the null hypothesis.

If in reality, the drug was ineffective, which type of error have the researchers committed?