## Symbolic Representation

Let's denote:
- \(x_1\) as the number of red shirts
- \(x_2\) as the number of green shirts

The objective is to maximize profit, where the profit per red shirt is $20 and per green shirt is $35. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 20x_1 + 35x_2 \]

The constraints based on the available resources are:
- Dye: \(2x_1 + 5x_2 \leq 1500\)
- Water: \(5x_1 + 8x_2 \leq 3000\)
- Worker minutes: \(20x_1 + 25x_2 \leq 8000\)
- Non-negativity: \(x_1 \geq 0, x_2 \geq 0\)

## Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'red shirts'), ('x2', 'green shirts')],
  'objective_function': '20*x1 + 35*x2',
  'constraints': [
    '2*x1 + 5*x2 <= 1500',
    '5*x1 + 8*x2 <= 3000',
    '20*x1 + 25*x2 <= 8000',
    'x1 >= 0',
    'x2 >= 0'
  ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("FancyClothingCo")

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

# Objective function: Maximize profit
model.setObjective(20*x1 + 35*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(2*x1 + 5*x2 <= 1500, name="dye_constraint")
model.addConstr(5*x1 + 8*x2 <= 3000, name="water_constraint")
model.addConstr(20*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: red shirts = {x1.varValue}, green shirts = {x2.varValue}")
  print(f"Maximal profit: ${model.objVal:.2f}")
else:
  print("No optimal solution found.")
```