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.

Let's define:
- `x1` as the number of bottles of almond bubble tea made per day.
- `x2` as the number of bottles of ginger bubble tea made per day.

The objective function, which aims to maximize profit, can be represented as:
\[ 5x_1 + 9x_2 \]

The constraints based on the problem description are:
1. Demand limit for almond bubble tea: \( x_1 \leq 120 \)
2. Demand limit for ginger bubble tea: \( x_2 \leq 200 \)
3. Total production limit: \( x_1 + x_2 \leq 300 \)
4. Non-negativity constraints: \( x_1 \geq 0, x_2 \geq 0 \)

The symbolic representation of the problem can be summarized as:
```json
{
  'sym_variables': [('x1', 'almond bubble tea'), ('x2', 'ginger bubble tea')],
  'objective_function': '5*x1 + 9*x2',
  'constraints': ['x1 <= 120', 'x2 <= 200', 'x1 + x2 <= 300', 'x1 >= 0', 'x2 >= 0']
}
```

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

```python
from gurobipy import *

# Create a new model
model = Model("Bubble_Tea_Optimization")

# Define variables
x1 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="almond_bubble_tea")
x2 = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name="ginger_bubble_tea")

# Set the objective function
model.setObjective(5*x1 + 9*x2, GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 <= 120, "almond_limit")
model.addConstr(x2 <= 200, "ginger_limit")
model.addConstr(x1 + x2 <= 300, "total_production_limit")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("No optimal solution found")
```