## Problem Description and Symbolic Representation

The problem involves minimizing an objective function subject to several constraints. The objective function to minimize is $4.1x_1 + 4.71x_2$, where $x_1$ represents the number of pen testers and $x_2$ represents the number of SOC operators.

The constraints are as follows:
- $2x_1 + 6x_2 \geq 5$ (network latency from pen testers and SOC operators should be at least 5 milliseconds)
- $2x_1 + 6x_2 \leq 34$ (network latency from pen testers and SOC operators should be at most 34 milliseconds, corrected from 25 ms as per initial problem description which seems to have a typo given the context of "upper_bound": 34)
- $-4x_1 + 2x_2 \geq 0$ (relationship between the number of pen testers and SOC operators)
- $x_1, x_2 \geq 0$ and are integers (non-fractional number of pen testers and SOC operators)

## Symbolic Representation

```json
{
    'sym_variables': [('x1', 'pen testers'), ('x2', 'SOC operators')],
    'objective_function': '4.1*x1 + 4.71*x2',
    'constraints': [
        '2*x1 + 6*x2 >= 5',
        '2*x1 + 6*x2 <= 34',
        '-4*x1 + 2*x2 >= 0',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(vtype=gurobi.GRB.INTEGER, name='pen_testers')
    x2 = model.addVar(vtype=gurobi.GRB.INTEGER, name='SOC_operators')

    # Objective function
    model.setObjective(4.1 * x1 + 4.71 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2 * x1 + 6 * x2 >= 5, name='latency_min')
    model.addConstr(2 * x1 + 6 * x2 <= 34, name='latency_max')
    model.addConstr(-4 * x1 + 2 * x2 >= 0, name='pen_testers_SOC_operators_relationship')

    # Non-negativity constraints are implicitly handled by Gurobi for integer variables

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'Number of pen testers: {x1.varValue}')
        print(f'Number of SOC operators: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```