To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints.

Let's denote:
- \(x_1\) as the number of pen testers,
- \(x_2\) as the number of SOC operators.

The objective function is to minimize: \(4.1x_1 + 4.71x_2\).

Constraints are as follows:
1. Network latency from pen testers and SOC operators should be at least 5 milliseconds: \(2x_1 + 6x_2 \geq 5\).
2. The same constraint is repeated, so we consider it only once.
3. \(-4x_1 + 2x_2 \geq 0\).
4. Network latency from pen testers and SOC operators should be at most 25 milliseconds: \(2x_1 + 6x_2 \leq 25\).
5. \(x_1\) and \(x_2\) must be integers (non-fractional).

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',
    '-4*x1 + 2*x2 >= 0',
    '2*x1 + 6*x2 <= 25',
    'x1 >= 0', 
    'x2 >= 0', 
    'x1 == int(x1)', 
    'x2 == int(x2)'
  ]
}
```

To solve this problem using Gurobi, we will write the Python code as follows:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="pen_testers")
x2 = m.addVar(vtype=GRB.INTEGER, name="SOC_operators")

# Set objective function
m.setObjective(4.1*x1 + 4.71*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 6*x2 >= 5, "latency_min")
m.addConstr(-4*x1 + 2*x2 >= 0, "other_constraint")
m.addConstr(2*x1 + 6*x2 <= 25, "latency_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Pen Testers: {x1.x}")
    print(f"SOC Operators: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```