## Problem Description and Symbolic Representation

The problem is to maximize the profit of a breakfast diner that makes pancakes and waffles, given the available flour and butter.

### Symbolic Representation

Let's define the symbolic variables:
- `x1`: pancakes
- `x2`: waffles

The objective function is to maximize the profit: `5*x1 + 7*x2`

The constraints are:
- Flour: `30*x1 + 50*x2 <= 5000`
- Butter: `10*x1 + 15*x2 <= 2000`
- Non-negativity: `x1 >= 0`, `x2 >= 0`

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'pancakes'), ('x2', 'waffles')], 
    'objective_function': '5*x1 + 7*x2', 
    'constraints': [
        '30*x1 + 50*x2 <= 5000', 
        '10*x1 + 15*x2 <= 2000', 
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
m = gp.Model("Pancakes_Waffles")

# Define the variables
x1 = m.addVar(name="pancakes", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = m.addVar(name="waffles", lb=0, vtype=gp.GRB.CONTINUOUS)

# Define the objective function
m.setObjective(5*x1 + 7*x2, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(30*x1 + 50*x2 <= 5000, name="flour_constraint")
m.addConstr(10*x1 + 15*x2 <= 2000, name="butter_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Pancakes: {x1.varValue}")
    print(f"Waffles: {x2.varValue}")
    print(f"Max Profit: {m.objVal}")
else:
    print("No optimal solution found.")
```