To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's denote:
- \(x_1\) as the kilograms of mixture A produced,
- \(x_2\) as the kilograms of mixture B produced.

Given information:
- 30 kilograms of gummy bears available,
- 25 kilograms of gummy worms available,
- Mixture A is 75% gummy bears and 25% gummy worms,
- Mixture B is 40% gummy bears and 60% gummy worms,
- Profit per kilogram of mixture A is $30,
- Profit per kilogram of mixture B is $40.

The objective function, which represents the total profit, can be written as:
\[ \text{Maximize:} \quad 30x_1 + 40x_2 \]

Constraints based on the availability of gummy bears and gummy worms:
1. For gummy bears: \(0.75x_1 + 0.40x_2 \leq 30\)
2. For gummy worms: \(0.25x_1 + 0.60x_2 \leq 25\)

Additionally, we have non-negativity constraints since the amount of mixture produced cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

Therefore, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'kilograms of mixture A'), ('x2', 'kilograms of mixture B')],
    'objective_function': '30*x1 + 40*x2',
    'constraints': [
        '0.75*x1 + 0.40*x2 <= 30',
        '0.25*x1 + 0.60*x2 <= 25',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

```python
from gurobipy import *

# Create a model
m = Model("Candy_Mixtures")

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

# Set the objective function
m.setObjective(30*x1 + 40*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.75*x1 + 0.40*x2 <= 30, name="Gummy_Bears")
m.addConstr(0.25*x1 + 0.60*x2 <= 25, name="Gummy_Worms")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Mixture A: {x1.x} kg")
    print(f"Mixture B: {x2.x} kg")
    print(f"Total Profit: ${30*x1.x + 40*x2.x}")
else:
    print("No optimal solution found")

```