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

- $x_1$ as the number of shipping containers sent.
- $x_2$ as the number of cargo planes sent.

The objective function aims to maximize the total number of products sent overseas. Given that each shipping container can take 1000 products and each cargo plane can take 800 products, the objective function can be represented algebraically as:

Maximize: $1000x_1 + 800x_2$

The constraints are:
1. The budget constraint: The total cost of sending shipping containers and cargo planes cannot exceed $20,000. Since the cost per shipping container is $5,000 and the cost per cargo plane is $6,000, this can be represented as $5,000x_1 + 6,000x_2 \leq 20,000$.
2. The constraint due to shipping delays: The number of shipping containers sent cannot exceed the number of cargo planes sent, which translates to $x_1 \leq x_2$.
3. Non-negativity constraints: Both $x_1$ and $x_2$ must be non-negative since you cannot send a negative number of containers or planes.

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'number of shipping containers sent'), ('x2', 'number of cargo planes sent')],
  'objective_function': 'Maximize: 1000*x1 + 800*x2',
  'constraints': ['5000*x1 + 6000*x2 <= 20000', 'x1 <= x2', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can use the following code:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(1000*x1 + 800*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5000*x1 + 6000*x2 <= 20000, "budget_constraint")
m.addConstr(x1 <= x2, "shipping_delay_constraint")
m.addConstr(x1 >= 0, "non_negativity_x1")
m.addConstr(x2 >= 0, "non_negativity_x2")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Shipping Containers: {x1.x}")
    print(f"Cargo Planes: {x2.x}")
    print(f"Total Products Sent: {1000*x1.x + 800*x2.x}")
else:
    print("No optimal solution found")
```