To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of interest (number of burgers and hot-dogs), formulating the objective function that represents the total revenue, and listing all constraints based on the available resources.

Let's denote:
- \(x_1\) as the number of burgers to be made,
- \(x_2\) as the number of hot-dogs to be made.

The objective function is to maximize revenue. Given that each burger generates $0.30 in revenue and each hot-dog generates $0.20, the objective function can be written as:
\[ \text{Maximize:} \quad 0.30x_1 + 0.20x_2 \]

The constraints are based on the availability of meat and binding agent:
- Each burger requires 3 units of meat, and each hot-dog requires 2 units of meat. The factory has 2000 units of meat available. Thus, the meat constraint is: \(3x_1 + 2x_2 \leq 2000\).
- Each burger requires 2 units of binding agent, and each hot-dog requires 1 unit of binding agent. The factory has 1800 units of binding agent available. Thus, the binding agent constraint is: \(2x_1 + x_2 \leq 1800\).

Additionally, since we cannot produce a negative number of burgers or hot-dogs, we have non-negativity constraints:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

The symbolic representation of the problem is thus:

```json
{
    'sym_variables': [('x1', 'number of burgers'), ('x2', 'number of hot-dogs')],
    'objective_function': '0.30*x1 + 0.20*x2',
    'constraints': ['3*x1 + 2*x2 <= 2000', '2*x1 + x2 <= 1800', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="burgers")
x2 = m.addVar(lb=0, name="hot_dogs")

# Set the objective function
m.setObjective(0.30*x1 + 0.20*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 2*x2 <= 2000, "meat_constraint")
m.addConstr(2*x1 + x2 <= 1800, "binding_agent_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of burgers: {x1.x}")
    print(f"Number of hot-dogs: {x2.x}")
    print(f"Total Revenue: ${0.30*x1.x + 0.20*x2.x:.2f}")
else:
    print("No optimal solution found")
```