To solve this optimization problem, we first need to understand and possibly simplify or clarify the constraints and objective function provided.

The objective function to minimize is: $5.1 \times \text{pen testers} + 3.94 \times \text{honeypots}$.

Given constraints:
1. Computational load of pen testers: $16 \times \text{pen testers}$.
2. Data confidentiality impact of pen testers: $2 \times \text{pen testers}$.
3. Computational load of honeypots: $13 \times \text{honeypots}$.
4. Data confidentiality impact of honeypots: $16 \times \text{honeypots}$.
5. Total computational load should be at least 69 TFLOPS: $16 \times \text{pen testers} + 13 \times \text{honeypots} \geq 69$.
6. The constraint that both pen testers and honeypots contribute to a total of at least 69 TFLOPS seems redundant with the previous point, so we'll consider it as part of the same requirement for computational load.
7. Total data confidentiality impact should be at least 15: $2 \times \text{pen testers} + 16 \times \text{honeypots} \geq 15$.
8. The constraint regarding data confidentiality being at least 15 is repeated, so we consider it as one requirement.
9. $-8 \times \text{pen testers} + 10 \times \text{honeypots} \geq 0$.
10. Total computational load should not exceed 127 TFLOPS: $16 \times \text{pen testers} + 13 \times \text{honeypots} \leq 127$.
11. Total data confidentiality impact should not exceed 65: $2 \times \text{pen testers} + 16 \times \text{honeypots} \leq 65$.
12. Pen testers must be a non-fractional (integer) number.
13. Honeypots must also be an integer amount.

Let's formulate this problem using Gurobi in Python:

```python
from gurobipy import *

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

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

# Objective function
m.setObjective(5.1 * pen_testers + 3.94 * honeypots, GRB.MINIMIZE)

# Constraints
m.addConstr(16 * pen_testers + 13 * honeypots >= 69, "Computational_Load_Min")
m.addConstr(2 * pen_testers + 16 * honeypots >= 15, "Data_Confidentiality_Min")
m.addConstr(-8 * pen_testers + 10 * honeypots >= 0, "Pen_Testers_Honeypots_Balance")
m.addConstr(16 * pen_testers + 13 * honeypots <= 127, "Computational_Load_Max")
m.addConstr(2 * pen_testers + 16 * honeypots <= 65, "Data_Confidentiality_Max")

# Solve the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Pen Testers: {pen_testers.x}")
    print(f"Honeypots: {honeypots.x}")
else:
    print("No optimal solution found.")
```