Practical (not theoretical) examples of where a 1 sided test would be valid? I know that it is generally accepted that the two-sided test is the "Gold Standard". However, I just wanted to see if there are real life, practical applications of the one-sided test in the real world, or if this only exists in academia.
Edit: "Generally accepted / Gold Standard" in the sense of being the default recommendation in the book Introduction to Statistical Learning, 2nd ed., p.558, footnote 8:

A one-sided $p$-value is the probability of seeing such an extreme value of the test statistic; e.g. the probability of seeing a test statistic greater than or equal to $T$=2.33. A two-sided $p$-value is the probability of seeing such an extreme value of the absolute test statistic; e.g. the probability of seeing a test statistic greater than or equal to 2.33 or less than or equal to −2.33. The default recommendation is to report a two-sided $p$-value rather than a one-sided $p$-value, unless there is a clear and compelling reason that only one direction of the test statistic is of scientific interest.


Some Scenarios
I wanted to focus on one particular part of your question:

I just wanted to see if there are real life, practical applications of
the 1 sided test in the real world

Here are some practical examples:

*

*"Is my company's starting salary larger than a rival company?"

*"Do students at my school receive less student aid than other schools?"

*"Are people from my graduating class taller than other classes before me?"

These are all questions that can be answered by one-sided tests. However, this is also where hypothesis testing is very important. Using the first example, we may have a litany of informal evidence that seems to suggest salaries are greater at another company (feedback from employees there, bigger offices at their company, etc.). Rather than just asking ourselves "are their salaries different from ours?" an easier question to answer may be the one already posed: are they higher? Knowing this information would be super useful if you decided to change companies down the road.
Remember that your chances of rejecting the null hypothesis in one direction increase as a result of how big the tail is compared to a two-tailed test, with the caveat that you can only test one region. Recall that a two-tailed rejection region is larger than a one-tailed region:

Having a strong idea of what the outcome should be ensures that this test answers your question in a more direct way than a two-tailed test.
Practical Example Using R
To simulate this specific scenario, I have created two normally distributed "salary" values for two banks: Bank of America (BOA) and CitiBank. Adjusting their means to only be slightly divergent, we can then test this with a t-test using a one-tail test.
#### Simulate Groups ####
set.seed(123)

group.1 <- rnorm(n = 1000,
                 mean = 100000,
                 sd = 10000)

group.2 <- rnorm(n = 1000,
                 mean = 120000,
                 sd = 5000)

df <- data.frame(CitiBank = group.1,
                 BOA = group.2)

#### Test Groups ####
t.test(group.2,
       group.1,
       alternative = "greater")

And you can see the test is significant:
    Welch Two Sample t-test

data:  group.2 and group.1
t = 56.98, df = 1484.2, p-value < 2.2e-16
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 19471.87      Inf
sample estimates:
mean of x mean of y 
 120212.3  100161.3 

However, if we plot the critical cutoff zones used for a two-tailed test and compare them to a one-tailed test:
#### Plot ####
library(tidyverse)
library(ggpubr)

p1 <- df %>% 
  gather() %>% 
  ggplot(aes(x=value,
             fill=key))+
  geom_density(alpha = .5,
               linewidth = 1)+
  theme_classic()+
  scale_fill_manual(values = c("black","white"))+
  geom_vline(aes(xintercept = mean(group.1)),
             color = "red",
             linetype = "dashed",
             linewidth = 1)+
  geom_vline(aes(xintercept = mean(group.2)),
             color = "red",
             linetype = "dashed",
             linewidth = 1)+
  geom_vline(aes(xintercept = mean(group.1) + 1.96*sd(group.1)),
             color = "blue",
             linetype = "dashed",
             linewidth = 1)+
  geom_vline(aes(xintercept = mean(group.1) - 1.96*sd(group.1)),
             color = "blue",
             linetype = "dashed",
             linewidth = 1)+
  labs(x="Salary",
       y = "Density",
       fill = "Group",
       title = "Salary Comparison with Two-Tailed Test")+
  scale_x_continuous(n.breaks = 10)

p2 <- df %>% 
  gather() %>% 
  ggplot(aes(x=value,
             fill=key))+
  geom_density(alpha = .5,
               linewidth = 1)+
  theme_classic()+
  scale_fill_manual(values = c("black","white"))+
  geom_vline(aes(xintercept = mean(group.1)),
             color = "red",
             linetype = "dashed",
             linewidth = 1)+
  geom_vline(aes(xintercept = mean(group.2)),
             color = "red",
             linetype = "dashed",
             linewidth = 1)+
  geom_vline(aes(xintercept = mean(group.1) + 1.645*sd(group.1)),
             color = "blue",
             linetype = "dashed",
             linewidth = 1)+
  labs(x="Salary",
       y = "Density",
       fill = "Group",
       title = "Salary Comparison with One-Tailed Test")+
  scale_x_continuous(n.breaks = 10)

ggarrange(p1,p2, ncol = 1)

You will get these plots:

The red dashed lines are the means of each group and the blue dashed lines are the critical regions to reject the null. The plot on top shows cutoffs for the two-tailed test whereas the plot on the bottom shows a one-tailed test. You can see that for the two-tailed test we barely pass the cutoff criterion. However, we have far surpassed it in the one-tailed case.
You can see clearly that Bank of America pays more. If you were to make a judgement call about which bank to work for, which would you choose? A two-tailed test may have given you a misfire if the means were slightly different from each other. This should highlight the practicality of the test as well as why strong hypotheses are helpful to answering these questions.
