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

Let's denote:
- $x_1$ as the number of intrusion analysts,
- $x_2$ as the number of honeypots.

The objective function to minimize is given by:
\[6.44x_1 + 9.58x_2\]

The constraints can be summarized as follows:
1. Power consumption from intrusion analysts and honeypots must be at least 40 kWh: $5x_1 + 18x_2 \geq 40$
2. Total power consumption should not exceed 86 kWh: $5x_1 + 18x_2 \leq 86$
3. Computational load from intrusion analysts and honeypots must be at least 13 TFLOPS: $4x_1 + 8x_2 \geq 13$
4. Total computational load should not exceed 59 TFLOPS: $4x_1 + 8x_2 \leq 59$
5. Six times the number of intrusion analysts minus four times the number of honeypots must be at least zero: $6x_1 - 4x_2 \geq 0$
6. Both $x_1$ and $x_2$ must be integers (non-fractional).

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'intrusion analysts'), ('x2', 'honeypots')],
    'objective_function': '6.44*x1 + 9.58*x2',
    'constraints': [
        '5*x1 + 18*x2 >= 40',
        '5*x1 + 18*x2 <= 86',
        '4*x1 + 8*x2 >= 13',
        '4*x1 + 8*x2 <= 59',
        '6*x1 - 4*x2 >= 0'
    ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")
x2 = m.addVar(vtype=GRB.INTEGER, name="honeypots")

# Set the objective function
m.setObjective(6.44*x1 + 9.58*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(5*x1 + 18*x2 >= 40, "power_consumption_min")
m.addConstr(5*x1 + 18*x2 <= 86, "power_consumption_max")
m.addConstr(4*x1 + 8*x2 >= 13, "computational_load_min")
m.addConstr(4*x1 + 8*x2 <= 59, "computational_load_max")
m.addConstr(6*x1 - 4*x2 >= 0, "analysts_honeypots_ratio")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Intrusion Analysts: {x1.x}")
    print(f"Honeypots: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found.")
```