## Step 1: Define the symbolic representation of the variables
The variables are 'security onions' and 'pen testers', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $8x_1 + 6x_2$.

## Step 3: Translate the constraints into symbolic notation
The constraints are:
1. $10x_1 + 18x_2 \geq 42$ (combined network latency impact),
2. $10x_1 + 18x_2 \geq 42$ (same as the first, redundant),
3. $11x_1 + 3x_2 \geq 30$ (combined data accessibility impact),
4. $11x_1 + 3x_2 \geq 30$ (same as the third, redundant),
5. $2x_1 - 5x_2 \geq 0$ (relationship between security onions and pen testers),
6. $10x_1 + 18x_2 \leq 58$ (network latency upper bound),
7. $11x_1 + 3x_2 \leq 85$ (data accessibility impact upper bound),
8. $x_1 \in \mathbb{Z}^+$ and $x_2 \in \mathbb{Z}^+$ (integer constraints).

## 4: Remove redundant constraints
Removing the redundant constraints, we have:
1. $10x_1 + 18x_2 \geq 42$,
2. $11x_1 + 3x_2 \geq 30$,
3. $2x_1 - 5x_2 \geq 0$,
4. $10x_1 + 18x_2 \leq 58$,
5. $11x_1 + 3x_2 \leq 85$.

## 5: Write down the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'security onions'), ('x2', 'pen testers')],
'objective_function': '8*x1 + 6*x2',
'constraints': [
    '10*x1 + 18*x2 >= 42',
    '11*x1 + 3*x2 >= 30',
    '2*x1 - 5*x2 >= 0',
    '10*x1 + 18*x2 <= 58',
    '11*x1 + 3*x2 <= 85',
    'x1 >= 0 and x1 is an integer',
    'x2 >= 0 and x2 is an integer'
]
}
```

## 6: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="security_onions", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="pen_testers", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(8 * x1 + 6 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(10 * x1 + 18 * x2 >= 42, name="network_latency_impact")
    model.addConstr(11 * x1 + 3 * x2 >= 30, name="data_accessibility_impact_min")
    model.addConstr(2 * x1 - 5 * x2 >= 0, name="relationship_between_security_onions_and_pen_testers")
    model.addConstr(10 * x1 + 18 * x2 <= 58, name="network_latency_upper_bound")
    model.addConstr(11 * x1 + 3 * x2 <= 85, name="data_accessibility_impact_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Security Onions: {x1.varValue}")
        print(f"Pen Testers: {x2.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```