To solve Bob's coffee shop problem, we need to convert the natural language description into a symbolic representation and then translate it into Gurobi code.

The variables in this problem are:
- The number of cups of coffee made per week: x1 = cups of coffee
- The number of cups of tea made per week: x2 = cups of tea

The objective function is to maximize profit, which can be represented as:
1*x1 + 2*x2 (since Bob makes a profit of $1 on each cup of coffee and $2 on each cup of tea)

The constraints are:
- Time constraint: 5*x1 + 3*x2 <= 500 (since it takes 5 minutes to make a cup of coffee and 3 minutes to make a cup of tea, and Bob only has 500 minutes per week)
- Product constraint: x1 + x2 <= 300 (since Bob can only make 300 total cups per week)
- Non-negativity constraints: x1 >= 0, x2 >= 0 (since Bob cannot make a negative number of cups)

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'cups of coffee'), ('x2', 'cups of tea')],
    'objective_function': '1*x1 + 2*x2',
    'constraints': ['5*x1 + 3*x2 <= 500', 'x1 + x2 <= 300', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(1*x1 + 2*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 3*x2 <= 500, name="time_constraint")
m.addConstr(x1 + x2 <= 300, name="product_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Cups of coffee: {x1.x}")
    print(f"Cups of tea: {x2.x}")
    print(f"Max profit: {m.objVal}")
else:
    print("No optimal solution found")
```