To convert the given natural language description of an optimization problem into a symbolic representation and then into functional Gurobi code, we follow these steps:

1. **Identify Variables**: The variables in this problem are 'security onions' and 'pen testers'. Let's represent them symbolically as \(x_0\) for 'security onions' and \(x_1\) for 'pen testers'.

2. **Objective Function**: The goal is to minimize \(8x_0 + 6x_1\), where \(x_0\) represents the number of security onions and \(x_1\) represents the number of pen testers.

3. **Constraints**:
    - Network latency constraints: 
        - Each security onion creates 10 ms of network latency, and each pen tester creates 18 ms.
        - The minimum combined network latency is 42 ms.
        - The maximum combined network latency is 111 ms (as per 'r0'), but there's also a mention of not exceeding 58 milliseconds, which seems to be the more relevant constraint given the context.
    - Data accessibility impact constraints:
        - Each security onion has an impact of 11, and each pen tester has an impact of 3.
        - The minimum combined data accessibility impact is 42 (but it's mentioned that this should be at least 30).
        - The maximum combined data accessibility impact is 92 (as per 'r1'), but there's also a mention of not exceeding 85, which seems to be the more relevant constraint given the context.
    - Additional constraints:
        - \(2x_0 - 5x_1 \geq 0\)
        - Both \(x_0\) and \(x_1\) must be whole numbers.

Given these steps, we can formulate the problem symbolically as follows:

```json
{
    'sym_variables': [('x0', 'security onions'), ('x1', 'pen testers')],
    'objective_function': '8*x0 + 6*x1',
    'constraints': [
        '10*x0 + 18*x1 >= 42',  # Minimum network latency
        '10*x0 + 18*x1 <= 58',  # Maximum network latency considering the more relevant constraint
        '11*x0 + 3*x1 >= 30',   # Minimum data accessibility impact
        '11*x0 + 3*x1 <= 85',   # Maximum data accessibility impact considering the more relevant constraint
        '2*x0 - 5*x1 >= 0'      # Additional constraint
    ]
}
```

Now, let's implement this in Gurobi:

```python
from gurobipy import *

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

# Add variables
x0 = m.addVar(vtype=GRB.INTEGER, name="security_onions")
x1 = m.addVar(vtype=GRB.INTEGER, name="pen_testers")

# Set objective function
m.setObjective(8*x0 + 6*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(10*x0 + 18*x1 >= 42, "min_network_latency")
m.addConstr(10*x0 + 18*x1 <= 58, "max_network_latency")
m.addConstr(11*x0 + 3*x1 >= 30, "min_data_accessibility")
m.addConstr(11*x0 + 3*x1 <= 85, "max_data_accessibility")
m.addConstr(2*x0 - 5*x1 >= 0, "additional_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Security Onions: {x0.x}")
    print(f"Pen Testers: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```