## Symbolic Representation

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

- \(x_1\) as the number of sour drops
- \(x_2\) as the number of sour belts

The objective is to minimize the cost of the mixture. Given that it costs $0.50 per sour drop and $0.40 per sour belt, the objective function can be represented as:

\[ \text{Minimize:} \quad 0.50x_1 + 0.40x_2 \]

The mixture must contain at least 30 units of sourness and 40 units of flavoring. Given that each sour drop has 2 units of sourness and 4 units of flavoring, and each sour belt has 4 units of sourness and 3 units of flavoring, we have:

\[ 2x_1 + 4x_2 \geq 30 \] (sourness constraint)
\[ 4x_1 + 3x_2 \geq 40 \] (flavoring constraint)

Additionally, the mixture can contain at most 5 sour belts:

\[ x_2 \leq 5 \] (sour belts constraint)

And, of course, \(x_1 \geq 0\) and \(x_2 \geq 0\) because the number of sour drops and belts cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'sour drops'), ('x2', 'sour belts')],
    'objective_function': '0.50*x1 + 0.40*x2',
    'constraints': [
        '2*x1 + 4*x2 >= 30',
        '4*x1 + 3*x2 >= 40',
        'x2 <= 5',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="sour_drops", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="sour_belts", lb=0, vtype=gp.GRB.CONTINUOUS)

# Objective function: Minimize cost
model.setObjective(0.50 * x1 + 0.40 * x2, gp.GRB.MINIMIZE)

# Constraints
model.addConstr(2 * x1 + 4 * x2 >= 30, name="sourness_constraint")
model.addConstr(4 * x1 + 3 * x2 >= 40, name="flavoring_constraint")
model.addConstr(x2 <= 5, name="sour_belts_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of sour drops: {x1.varValue}")
    print(f"Number of sour belts: {x2.varValue}")
    print(f"Optimal cost: {model.objVal}")
else:
    print("No optimal solution found.")
```