To solve the given optimization problem using Gurobi, we first need to understand and possibly simplify or clarify the constraints provided. The objective function to be minimized is:

\[ 1 \times \text{logistics companies} + 9 \times \text{signal platoons} + 6 \times \text{transportation companies} \]

Subject to several constraints regarding mobility ratings and a constraint involving logistics and signal platoons. Let's denote:
- \(L\) as the number of logistics companies,
- \(S\) as the number of signal platoons,
- \(T\) as the number of transportation companies.

The constraints can be summarized as follows:
1. Mobility rating constraints:
   - \(4.57L + 1.6S \geq 31\)
   - \(4.57L + 3.13T \geq 23\)
   - \(4.57L + 1.6S + 3.13T \geq 55\)
2. Additional constraint:
   - \(5L - 2S \geq 0\)
3. Integrity constraints:
   - \(L, S, T\) must be whole numbers (non-negative integers).

Given these constraints and the objective function, we can now formulate this problem in Gurobi.

```python
from gurobipy import *

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

# Define variables
L = m.addVar(vtype=GRB.INTEGER, name="logistics_companies")
S = m.addVar(vtype=GRB.INTEGER, name="signal_platoons")
T = m.addVar(vtype=GRB.INTEGER, name="transportation_companies")

# Objective function
m.setObjective(1*L + 9*S + 6*T, GRB.MINIMIZE)

# Constraints
m.addConstr(4.57*L + 1.6*S >= 31, name="mobility_rating_1")
m.addConstr(4.57*L + 3.13*T >= 23, name="mobility_rating_2")
m.addConstr(4.57*L + 1.6*S + 3.13*T >= 55, name="total_mobility_rating")
m.addConstr(5*L - 2*S >= 0, name="additional_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Logistics Companies: {L.x}")
    print(f"Signal Platoons: {S.x}")
    print(f"Transportation Companies: {T.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```