## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of shipping containers sent.
- $x_2$ represents the number of cargo planes sent.

The objective is to maximize the total number of products sent, which is $1000x_1 + 800x_2$.

## Step 2: Define the constraints

The constraints given are:
1. The cost per shipping container is $5000 and per cargo plane is $6000, with a total budget of $20000: $5000x_1 + 6000x_2 \leq 20000$.
2. The number of shipping containers sent cannot exceed the number of cargo planes sent: $x_1 \leq x_2$.
3. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, since the number of containers and planes cannot be negative.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'shipping containers'), ('x2', 'cargo planes')],
    'objective_function': '1000*x1 + 800*x2',
    'constraints': [
        '5000*x1 + 6000*x2 <= 20000',
        'x1 <= x2',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code

To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, ub=None, obj=0)  # Number of shipping containers
    x2 = model.addVar(name="x2", lb=0, ub=None, obj=0)  # Number of cargo planes

    # Set the objective function coefficients
    x1.obj = 1000
    x2.obj = 800

    # Add constraints
    model.addConstr(5000 * x1 + 6000 * x2 <= 20000, name="budget_constraint")
    model.addConstr(x1 <= x2, name="container_plane_constraint")

    # Set the model to maximize the objective function
    model.modelSense = gurobi.GRB.MAXIMIZE

    # Optimize the model
    model.optimize()

    # Print the status of the model
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of shipping containers: {x1.x}")
        print(f"Number of cargo planes: {x2.x}")
        print(f"Total products sent: {1000*x1.x + 800*x2.x}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```