To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

### Symbolic Representation:

- **Variables:**
  - `x1`: Number of bottles of oolong tea made per day.
  - `x2`: Number of bottles of green tea made per day.

- **Objective Function:**
  The objective is to maximize profit. Given that the profit per bottle of oolong tea is $30 and the profit per bottle of green tea is $20, the objective function can be represented as:
  ```
  Maximize: 30x1 + 20x2
  ```

- **Constraints:**
  1. Demand for oolong tea: `x1 ≤ 100`
  2. Demand for green tea: `x2 ≤ 80`
  3. Supply constraint (total bottles of either type): `x1 + x2 ≤ 150`
  4. Non-negativity constraints (since the number of bottles cannot be negative): `x1 ≥ 0`, `x2 ≥ 0`

### Symbolic Representation in JSON Format:
```json
{
  'sym_variables': [('x1', 'Number of bottles of oolong tea per day'), ('x2', 'Number of bottles of green tea per day')],
  'objective_function': '30x1 + 20x2',
  'constraints': ['x1 <= 100', 'x2 <= 80', 'x1 + x2 <= 150', 'x1 >= 0', 'x2 >= 0']
}
```

### Gurobi Code:
To solve this problem using Gurobi in Python, we'll use the following code:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(30*x1 + 20*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 <= 100, "Oolong_Demand")
m.addConstr(x2 <= 80, "Green_Demand")
m.addConstr(x1 + x2 <= 150, "Total_Supply")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Oolong Tea: {x1.x}")
    print(f"Green Tea: {x2.x}")
    print(f"Maximum Profit: {m.objVal}")
else:
    print("No optimal solution found")
```