To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$: Number of Earl Grey teabags
- $x_2$: Number of English Breakfast teabags

The objective function is to maximize profit, given by $0.30x_1 + 0.25x_2$.

The constraints are:
1. Black tea availability: $25x_1 + 20x_2 \leq 3000$
2. Demand constraint: $x_1 \geq 4x_2$
3. Minimum English Breakfast teabags: $x_2 \geq 20$
4. Non-negativity constraints: $x_1, x_2 \geq 0$

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'Number of Earl Grey teabags'), ('x2', 'Number of 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']
}
```

Now, let's implement this problem in Gurobi using Python:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="Earl_Grey")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="English_Breakfast")

# Set objective function
m.setObjective(0.30*x1 + 0.25*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(25*x1 + 20*x2 <= 3000, "Black_Tea_Availability")
m.addConstr(x1 >= 4*x2, "Demand_Constraint")
m.addConstr(x2 >= 20, "Minimum_English_Breakfast")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Earl Grey teabags:", x1.x)
    print("English Breakfast teabags:", x2.x)
else:
    print("No optimal solution found")
```