## Step 1: Define the symbolic representation of the problem
Let's denote:
- $x_1$ as the number of short shots,
- $x_2$ as the number of long shots.

The objective is to maximize the score, which can be represented as $2x_1 + 5x_2$.

## Step 2: List the constraints based on the problem description
The constraints are:
1. Total shots: $x_1 + x_2 \leq 14$,
2. Minimum short shots: $x_1 \geq 5$,
3. Minimum long shots: $x_2 \geq 2$,
4. Maximum short shots: $x_1 \leq 8$,
5. Maximum long shots: $x_2 \leq 8$.

## 3: Symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'short shots'), ('x2', 'long shots')],
    'objective_function': '2*x1 + 5*x2',
    'constraints': [
        'x1 + x2 <= 14',
        'x1 >= 5',
        'x2 >= 2',
        'x1 <= 8',
        'x2 <= 8'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python
To solve this linear programming problem using Gurobi, we will use the Gurobi Python interface. First, ensure you have Gurobi installed in your Python environment. You can install it via pip: `pip install gurobi`.

```python
import gurobi as gp

# Create a new model
m = gp.Model("shot_game")

# Define variables
x1 = m.addVar(lb=5, ub=8, name="short_shots")  # short shots
x2 = m.addVar(lb=2, ub=8, name="long_shots")  # long shots

# Objective function: maximize 2*x1 + 5*x2
m.setObjective(2*x1 + 5*x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 + x2 <= 14, name="total_shots")

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal short shots: {x1.varValue}")
    print(f"Optimal long shots: {x2.varValue}")
    print(f"Maximum score: {2*x1.varValue + 5*x2.varValue}")
else:
    print("The model is infeasible")
```