## Symbolic Representation

To solve the problem described, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, as well as the constraints.

- **Variables:**
  - $x_1$ = Number of regular truck trips
  - $x_2$ = Number of refrigerated truck trips

- **Objective Function:** Maximize the total number of potato packages transported.
  - The number of packages transported by regular trucks = $70x_1$
  - The number of packages transported by refrigerated trucks = $100x_2$
  - **Objective Function:** $70x_1 + 100x_2$

- **Constraints:**
  1. Cost constraint: $50x_1 + 70x_2 \leq 5000$
  2. Refrigerated trips constraint: $x_2 \leq x_1$
  3. Non-negativity constraint: $x_1 \geq 0, x_2 \geq 0$ (Implicitly assumed as trips cannot be negative)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'regular truck trips'), ('x2', 'refrigerated truck trips')],
    'objective_function': '70*x1 + 100*x2',
    'constraints': [
        '50*x1 + 70*x2 <= 5000',
        'x2 <= x1',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_potato_transport_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="x1", obj=0, lb=0, vtype=gurobi.GRB.INTEGER)  # Regular truck trips
    x2 = model.addVar(name="x2", obj=0, lb=0, vtype=gurobi.GRB.INTEGER)  # Refrigerated truck trips

    # Objective function: Maximize 70*x1 + 100*x2
    model.setObjective(70*x1 + 100*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(50*x1 + 70*x2 <= 5000, name="cost_constraint")
    model.addConstr(x2 <= x1, name="refrigerated_trips_constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Regular truck trips: {x1.varValue}")
        print(f"Refrigerated truck trips: {x2.varValue}")
        print(f"Total packages: {70*x1.varValue + 100*x2.varValue}")
    else:
        print("No optimal solution found.")

solve_potato_transport_problem()
```