To solve Tom's problem, we need to convert the natural language description into a symbolic representation and then translate it into Gurobi code. Let's break down the problem:

- **Variables**: Let $x_1$ represent the number of slow shots and $x_2$ represent the number of quick shots.
- **Objective Function**: The objective is to maximize Tom's score, which can be represented as $3x_1 + 6x_2$, since a slow shot is worth 3 points and a quick shot is worth 6 points.
- **Constraints**:
  - Tom can take at most 20 shots: $x_1 + x_2 \leq 20$
  - He must take at least 8 slow shots: $x_1 \geq 8$
  - He must take at least 5 quick shots: $x_2 \geq 5$
  - Due to time restrictions, he can take at most 12 slow shots: $x_1 \leq 12$
  - Due to time restrictions, he can take at most 12 quick shots: $x_2 \leq 12$

The symbolic representation of the problem is:

```json
{
  'sym_variables': [('x1', 'number of slow shots'), ('x2', 'number of quick shots')],
  'objective_function': '3*x1 + 6*x2',
  'constraints': ['x1 + x2 <= 20', 'x1 >= 8', 'x2 >= 5', 'x1 <= 12', 'x2 <= 12']
}
```

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

# Create a model
m = Model("Tom_Shots")

# Define variables
x1 = m.addVar(lb=8, ub=12, vtype=GRB.INTEGER, name="slow_shots")
x2 = m.addVar(lb=5, ub=12, vtype=GRB.INTEGER, name="quick_shots")

# Set the objective function
m.setObjective(3*x1 + 6*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 20, "total_shots")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Slow shots: {x1.x}")
    print(f"Quick shots: {x2.x}")
    print(f"Total score: {3*x1.x + 6*x2.x}")
else:
    print("No optimal solution found")
```