To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints using these variables.

Let's define:
- \(x_1\) as the number of train trips,
- \(x_2\) as the number of car trips.

The objective is to maximize the total number of oranges sent, which can be represented by the objective function: \(500x_1 + 200x_2\).

Given the constraints:
1. The cost constraint: Each train trip costs $50, and each car trip costs $30, with a total budget of $500. This translates to: \(50x_1 + 30x_2 \leq 500\).
2. The scheduling issue constraint: The number of train trips cannot exceed the number of car trips, which translates to: \(x_1 \leq x_2\).
3. Non-negativity constraints: Since the number of trips cannot be negative, we have: \(x_1 \geq 0\) and \(x_2 \geq 0\).

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of train trips'), ('x2', 'number of car trips')],
    'objective_function': '500*x1 + 200*x2',
    'constraints': ['50*x1 + 30*x2 <= 500', 'x1 <= x2', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Orange Farm Optimization")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="train_trips")
x2 = m.addVar(vtype=GRB.INTEGER, name="car_trips")

# Set the objective function
m.setObjective(500*x1 + 200*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(50*x1 + 30*x2 <= 500, "budget_constraint")
m.addConstr(x1 <= x2, "scheduling_constraint")
m.addConstr(x1 >= 0, "non_neg_train_trips")
m.addConstr(x2 >= 0, "non_neg_car_trips")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of train trips: {x1.x}")
    print(f"Number of car trips: {x2.x}")
    print(f"Total oranges sent: {500*x1.x + 200*x2.x}")
else:
    print("No optimal solution found")
```