## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, as well as the constraints.

### Variables
- $x_1$: Number of blue t-shirts
- $x_2$: Number of dark blue t-shirts

### Objective Function
The objective is to maximize profit. Given that the profit per blue t-shirt is $10 and per dark blue t-shirt is $15, the objective function can be written as:
\[ \text{Maximize:} \quad 10x_1 + 15x_2 \]

### Constraints
1. Dye constraint: $3x_1 + 5x_2 \leq 1000$
2. Water constraint: $5x_1 + 4x_2 \leq 1200$
3. Worker minutes constraint: $30x_1 + 25x_2 \leq 8000$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'blue t-shirts'), ('x2', 'dark blue t-shirts')],
    'objective_function': '10*x1 + 15*x2',
    'constraints': [
        '3*x1 + 5*x2 <= 1000',
        '5*x1 + 4*x2 <= 1200',
        '30*x1 + 25*x2 <= 8000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("T-Shirt_Production")

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

# Objective function: Maximize profit
model.setObjective(10*x1 + 15*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(3*x1 + 5*x2 <= 1000, name="dye_constraint")
model.addConstr(5*x1 + 4*x2 <= 1200, name="water_constraint")
model.addConstr(30*x1 + 25*x2 <= 8000, name="worker_minutes_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
    print(f"Maximum profit: ${10*x1.varValue + 15*x2.varValue:.2f}")
else:
    print("The model is infeasible.")
```