## Step 1: Define the symbolic representation of the variables
The variables are 'logistics companies', 'signal platoons', and 'transportation companies'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1 \times x_1 + 9 \times x_2 + 6 \times x_3$.

## 3: Define the constraints in symbolic notation
The constraints given are:
1. The mobility rating of logistics companies is 4.57: $4.57x_1$ (but this seems to be an attribute, not a constraint in the traditional sense, as it's a fixed value).
2. The mobility rating of signal platoons is 1.6 each: $1.6x_2$ (similarly, this is an attribute).
3. The mobility rating of transportation companies is 3.13: $3.13x_3$ (attribute).
4. The total combined mobility rating from logistics companies and signal platoons must be equal to or greater than 31: $4.57x_1 + 1.6x_2 \geq 31$.
5. The total combined mobility rating from logistics companies and transportation companies has to be greater than or equal to 23: $4.57x_1 + 3.13x_3 \geq 23$.
6. The total combined mobility rating from logistics companies plus signal platoons plus transportation companies has to be at minimum 55: $4.57x_1 + 1.6x_2 + 3.13x_3 \geq 55$.
7. $5x_1 - 2x_2 \geq 0$.
8. $x_1, x_2, x_3$ must be whole numbers.

## 4: Correcting and Clarifying Constraints
Upon closer inspection, constraints 1, 2, and 3 are not traditional constraints but rather definitions of the mobility ratings per unit of each type of company/platoon. The actual constraints from the problem statement are:
- $4.57x_1 + 1.6x_2 \geq 31$
- $4.57x_1 + 3.13x_3 \geq 23$
- $4.57x_1 + 1.6x_2 + 3.13x_3 \geq 55$
- $5x_1 - 2x_2 \geq 0$
- $x_1, x_2, x_3$ are integers.

## 5: Symbolic Representation
The symbolic representation of the problem is:
- Variables: $x_1$ for logistics companies, $x_2$ for signal platoons, $x_3$ for transportation companies.
- Objective Function: Minimize $x_1 + 9x_2 + 6x_3$.
- Constraints:
  1. $4.57x_1 + 1.6x_2 \geq 31$
  2. $4.57x_1 + 3.13x_3 \geq 23$
  3. $4.57x_1 + 1.6x_2 + 3.13x_3 \geq 55$
  4. $5x_1 - 2x_2 \geq 0$
  5. $x_1, x_2, x_3$ are integers.

## 6: Gurobi Code
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x1 = m.addVar(name="logistics_companies", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="signal_platoons", vtype=gurobi.GRB.INTEGER)
x3 = m.addVar(name="transportation_companies", vtype=gurobi.GRB.INTEGER)

# Objective function
m.setObjective(x1 + 9*x2 + 6*x3, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(4.57*x1 + 1.6*x2 >= 31)
m.addConstr(4.57*x1 + 3.13*x3 >= 23)
m.addConstr(4.57*x1 + 1.6*x2 + 3.13*x3 >= 55)
m.addConstr(5*x1 - 2*x2 >= 0)

# Optimize
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Logistics Companies: ", x1.varValue)
    print("Signal Platoons: ", x2.varValue)
    print("Transportation Companies: ", x3.varValue)
else:
    print("The problem is infeasible")
```

## 7: Final Output
```json
{
    'sym_variables': [('x1', 'logistics companies'), ('x2', 'signal platoons'), ('x3', 'transportation companies')],
    'objective_function': 'x1 + 9x2 + 6x3',
    'constraints': [
        '4.57x1 + 1.6x2 >= 31',
        '4.57x1 + 3.13x3 >= 23',
        '4.57x1 + 1.6x2 + 3.13x3 >= 55',
        '5x1 - 2x2 >= 0'
    ]
}
```