To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of vehicle tickets sold,
- $x_2$ as the number of passenger tickets sold.

The objective function is to maximize profit. Given that a profit of $130 is made for each vehicle and $60 for each passenger, the objective function can be represented algebraically as:

$$
\text{Maximize: } 130x_1 + 60x_2
$$

The constraints given in the problem are:
1. The company can sell at most 200 tickets: $x_1 + x_2 \leq 200$.
2. A minimum of 20 tickets are reserved for vehicles: $x_1 \geq 20$.
3. At least 4 times as many tickets are sold for passengers than for vehicles: $x_2 \geq 4x_1$.

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of vehicle tickets'), ('x2', 'number of passenger tickets')],
    'objective_function': '130*x1 + 60*x2',
    'constraints': [
        'x1 + x2 <= 200',
        'x1 >= 20',
        'x2 >= 4*x1'
    ]
}
```

Given this symbolic representation, we can now translate it into Gurobi code in Python to solve the optimization problem.

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(name='vehicle_tickets', vtype=GRB.INTEGER, lb=0)
x2 = m.addVar(name='passenger_tickets', vtype=GRB.INTEGER, lb=0)

# Set the objective function
m.setObjective(130*x1 + 60*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 200, name='total_tickets')
m.addConstr(x1 >= 20, name='min_vehicle_tickets')
m.addConstr(x2 >= 4*x1, name='passenger_to_vehicle_ratio')

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution found: {m.objVal}")
    print(f"Vehicle tickets sold: {x1.x}")
    print(f"Passenger tickets sold: {x2.x}")
else:
    print("No optimal solution found")
```