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

Let's denote:
- $x_1$ as the number of packages of adhesives produced daily
- $x_2$ as the number of packages of plasticizers produced daily

The objective is to maximize revenue. The revenue from selling $x_1$ packages of adhesives is $8.5x_1$ and from selling $x_2$ packages of plasticizers is $11.5x_2$. Therefore, the objective function to maximize is $8.5x_1 + 11.5x_2$.

## Step 2: Define the constraints based on the problem description

1. The automatic device is available for at most 450 minutes. Since it takes 6 minutes to produce a package of adhesives and 8 minutes to produce a package of plasticizers, we have $6x_1 + 8x_2 \leq 450$.
2. The human-operated device is available for at most 450 minutes. Since it takes 5 minutes to produce a package of adhesives and 4 minutes to produce a package of plasticizers, we have $5x_1 + 4x_2 \leq 450$.
3. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, because the number of packages produced cannot be negative.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'packages of adhesives'), ('x2', 'packages of plasticizers')],
    'objective_function': '8.5*x1 + 11.5*x2',
    'constraints': [
        '6*x1 + 8*x2 <= 450',
        '5*x1 + 4*x2 <= 450',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python

```python
import gurobipy as gp

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

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

# Objective function: maximize revenue
model.setObjective(8.5 * x1 + 11.5 * x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(6 * x1 + 8 * x2 <= 450, name="automatic_device_constraint")
model.addConstr(5 * x1 + 4 * x2 <= 450, name="human_operated_device_constraint")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Packages of adhesives to produce: {x1.varValue}")
    print(f"Packages of plasticizers to produce: {x2.varValue}")
    print(f"Max revenue: {model.objVal}")
else:
    print("No optimal solution found.")
```