To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of sour drops as \(x_1\) and the number of sour belts as \(x_2\).

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 algebraically as:
\[0.5x_1 + 0.4x_2\]

The constraints based on the natural language description are:
1. The mixture contains at least 30 units of sourness: \(2x_1 + 4x_2 \geq 30\)
2. The mixture contains at least 40 units of flavoring: \(4x_1 + 3x_2 \geq 40\)
3. The mixture can contain at most 5 sour belts: \(x_2 \leq 5\)

Non-negativity constraints also apply since we cannot have a negative number of items:
\[x_1 \geq 0, x_2 \geq 0\]

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of sour drops'), ('x2', 'number of sour belts')],
    'objective_function': '0.5*x1 + 0.4*x2',
    'constraints': ['2*x1 + 4*x2 >= 30', '4*x1 + 3*x2 >= 40', 'x2 <= 5', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="sour_drops")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="sour_belts")

# Set the objective function
m.setObjective(0.5*x1 + 0.4*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + 4*x2 >= 30, "Sourness_Constraint")
m.addConstr(4*x1 + 3*x2 >= 40, "Flavoring_Constraint")
m.addConstr(x2 <= 5, "Max_Sour_Belts_Constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of sour drops: {x1.x}")
    print(f"Number of sour belts: {x2.x}")
    print(f"Total cost: ${0.5*x1.x + 0.4*x2.x:.2f}")
else:
    print("No optimal solution found")
```