To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

Let's denote:
- $x_0$ as the number of network administrators,
- $x_1$ as the number of intrusion analysts.

The objective function is given as: $1.55x_0 + 8.05x_1$.

The constraints can be listed as follows:
1. Data integrity impact constraint: $0.29x_0 + 3.64x_1 \geq 12$
2. Cost constraint (at least $6): $1.96x_0 + 4.3x_1 \geq 6$
3. Minimum total cost constraint: Same as the previous one, so we consider it only once.
4. Computational load constraint (at least 3 TFLOPs): $0.04x_0 + 1.27x_1 \geq 3$
5. Additional linear constraint: $2x_0 - 5x_1 \geq 0$
6. Upper bound on data integrity impact: $0.29x_0 + 3.64x_1 \leq 20$
7. Upper bound on total cost: $1.96x_0 + 4.3x_1 \leq 22$
8. Upper bound on computational load: $0.04x_0 + 1.27x_1 \leq 15$

Now, let's represent this problem symbolically and in Gurobi code:

```json
{
    'sym_variables': [('x0', 'network administrators'), ('x1', 'intrusion analysts')],
    'objective_function': '1.55*x0 + 8.05*x1',
    'constraints': [
        '0.29*x0 + 3.64*x1 >= 12',
        '1.96*x0 + 4.3*x1 >= 6',
        '0.04*x0 + 1.27*x1 >= 3',
        '2*x0 - 5*x1 >= 0',
        '0.29*x0 + 3.64*x1 <= 20',
        '1.96*x0 + 4.3*x1 <= 22',
        '0.04*x0 + 1.27*x1 <= 15'
    ]
}
```

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

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="network_administrators")
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")

# Set the objective function
m.setObjective(1.55*x0 + 8.05*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.29*x0 + 3.64*x1 >= 12, "data_integrity_constraint")
m.addConstr(1.96*x0 + 4.3*x1 >= 6, "minimum_cost_constraint")
m.addConstr(0.04*x0 + 1.27*x1 >= 3, "computational_load_constraint")
m.addConstr(2*x0 - 5*x1 >= 0, "additional_linear_constraint")
m.addConstr(0.29*x0 + 3.64*x1 <= 20, "upper_bound_data_integrity")
m.addConstr(1.96*x0 + 4.3*x1 <= 22, "upper_bound_total_cost")
m.addConstr(0.04*x0 + 1.27*x1 <= 15, "upper_bound_computational_load")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Network Administrators: {x0.x}")
    print(f"Intrusion Analysts: {x1.x}")
else:
    print("No optimal solution found.")

```