## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of earl grey teabags
- $x_2$ represents the number of English breakfast teabags

## Step 2: Formulate the objective function
The profit per earl grey teabag is $0.30 and the profit per English breakfast teabag is $0.25. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 0.30x_1 + 0.25x_2 \]

## 3: Define the constraints
1. Black tea availability: $25x_1 + 20x_2 \leq 3000$
2. Demand for earl grey teabags vs English breakfast teabags: $x_1 \geq 4x_2$
3. Minimum English breakfast teabags: $x_2 \geq 20$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 4: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'earl grey teabags'), ('x2', 'English breakfast teabags')],
    'objective_function': '0.30*x1 + 0.25*x2',
    'constraints': [
        '25*x1 + 20*x2 <= 3000',
        'x1 >= 4*x2',
        'x2 >= 20',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 5: Gurobi code to solve the problem
```python
import gurobi

def solve_tea_bag_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='earl_grey', lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='english_breakfast', lb=0, vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(0.30 * x1 + 0.25 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(25 * x1 + 20 * x2 <= 3000, name='black_tea_availability')
    model.addConstr(x1 >= 4 * x2, name='earl_grey_demand')
    model.addConstr(x2 >= 20, name='english_breakfast_min')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: earl grey = {x1.varValue}, english breakfast = {x2.varValue}")
    else:
        print("No optimal solution found")

solve_tea_bag_problem()
```