## Step 1: Define the symbolic representation of the problem

Let's denote the number of low quality teas as $x_1$, the number of medium quality teas as $x_2$, and the number of high quality teas as $x_3$. The profit per low quality tea is $1, per medium quality tea is $3, and per high quality tea is $5.

## Step 2: Formulate the objective function

The objective function to maximize profits is: $1x_1 + 3x_2 + 5x_3$.

## Step 3: Define the constraints

The company has available 200 units of rare additives and 400 units of tea leaves. The constraints are:
- $2x_1 + 3x_2 + 4x_3 \leq 200$ (rare additives constraint)
- $6x_1 + 7x_2 + 8x_3 \leq 400$ (tea leaves constraint)
- $x_1, x_2, x_3 \geq 0$ (non-negativity constraint)

## 4: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'low quality tea'), ('x2', 'medium quality tea'), ('x3', 'high quality tea')],
    'objective_function': '1*x1 + 3*x2 + 5*x3',
    'constraints': [
        '2*x1 + 3*x2 + 4*x3 <= 200',
        '6*x1 + 7*x2 + 8*x3 <= 400',
        'x1 >= 0',
        'x2 >= 0',
        'x3 >= 0'
    ]
}
```

## 5: Gurobi code in Python

```python
import gurobi

def solve_tea_company_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="low_quality_tea", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="medium_quality_tea", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x3 = model.addVar(name="high_quality_tea", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(1*x1 + 3*x2 + 5*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x1 + 3*x2 + 4*x3 <= 200, name="rare_additives_constraint")
    model.addConstr(6*x1 + 7*x2 + 8*x3 <= 400, name="tea_leaves_constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Low quality tea: {x1.varValue}")
        print(f"Medium quality tea: {x2.varValue}")
        print(f"High quality tea: {x3.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_tea_company_problem()
```