## Step 1: Define the symbolic representation of the variables
The variables are 'pen testers', 'SOC operators', 'network administrators', and 'deployed decoys'. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1 \cdot x_1 + 9 \cdot x_2 + 3 \cdot x_3 + 5 \cdot x_4$.

## Step 3: List the constraints in symbolic notation
1. $9.24x_1 \leq 131$
2. $10.88x_2 \leq 131$
3. $12.55x_3 \leq 131$
4. $4.75x_4 \leq 131$
5. $9.24x_1 + 4.75x_4 \geq 24$
6. $12.55x_3 + 4.75x_4 \geq 15$
7. $9.24x_1 + 10.88x_2 \geq 11$
8. $9.24x_1 + 10.88x_2 + 12.55x_3 + 4.75x_4 \geq 11$
9. $6x_1 - 10x_4 \geq 0$
10. $9.24x_1 \leq 123$
11. $12.55x_3 \leq 123$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'pen testers'), 
        ('x2', 'SOC operators'), 
        ('x3', 'network administrators'), 
        ('x4', 'deployed decoys')
    ], 
    'objective_function': '1*x1 + 9*x2 + 3*x3 + 5*x4', 
    'constraints': [
        '9.24*x1 <= 131', 
        '10.88*x2 <= 131', 
        '12.55*x3 <= 131', 
        '4.75*x4 <= 131', 
        '9.24*x1 + 4.75*x4 >= 24', 
        '12.55*x3 + 4.75*x4 >= 15', 
        '9.24*x1 + 10.88*x2 >= 11', 
        '9.24*x1 + 10.88*x2 + 12.55*x3 + 4.75*x4 >= 11', 
        '6*x1 - 10*x4 >= 0', 
        '9.24*x1 <= 123', 
        '12.55*x3 <= 123'
    ]
}
```

## 5: Write the Gurobi code for the optimization problem
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name='pen_testers', vtype=gurobi.GRB.INTEGER)  # pen testers
x2 = m.addVar(name='SOC_operators', vtype=gurobi.GRB.INTEGER)  # SOC operators
x3 = m.addVar(name='network_administrators', vtype=gurobi.GRB.INTEGER)  # network administrators
x4 = m.addVar(name='deployed_decoys', vtype=gurobi.GRB.INTEGER)  # deployed decoys

# Objective function
m.setObjective(1 * x1 + 9 * x2 + 3 * x3 + 5 * x4, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(9.24 * x1 <= 131)
m.addConstr(10.88 * x2 <= 131)
m.addConstr(12.55 * x3 <= 131)
m.addConstr(4.75 * x4 <= 131)
m.addConstr(9.24 * x1 + 4.75 * x4 >= 24)
m.addConstr(12.55 * x3 + 4.75 * x4 >= 15)
m.addConstr(9.24 * x1 + 10.88 * x2 >= 11)
m.addConstr(9.24 * x1 + 10.88 * x2 + 12.55 * x3 + 4.75 * x4 >= 11)
m.addConstr(6 * x1 - 10 * x4 >= 0)
m.addConstr(9.24 * x1 <= 123)
m.addConstr(12.55 * x3 <= 123)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('pen testers: ', x1.varValue)
    print('SOC operators: ', x2.varValue)
    print('network administrators: ', x3.varValue)
    print('deployed decoys: ', x4.varValue)
else:
    print('The model is infeasible')
```