To solve the given optimization problem, we need to translate the natural language description into a mathematical formulation that can be expressed in Gurobi code. The goal is to maximize the total return on investment under the given constraints.

Let's denote:
- \(B\) as the amount invested in the biotech industry,
- \(F\) as the amount invested in the food industry,
- \(FI\) as the amount invested in the finance industry, and
- \(H\) as the amount invested in the health care industry.

The objective function to maximize is the total return on investment:
\[ \text{Maximize} \quad 0.04B + 0.06F + 0.08FI + 0.10H \]

Subject to the constraints:
1. The total amount invested does not exceed $1,000,000:
\[ B + F + FI + H \leq 1000000 \]
2. The amount invested in the health care industry does not exceed the amount invested in the biotech industry:
\[ H \leq B \]
3. The amount invested in the food industry cannot exceed the amount invested in the finance industry:
\[ F \leq FI \]
4. At most 30% of the investment can be in the health care industry:
\[ H \leq 0.30 \times 1000000 \]
\[ H \leq 300000 \]

All variables are non-negative since you cannot invest a negative amount:
\[ B, F, FI, H \geq 0 \]

This problem can be formulated and solved using Gurobi in Python as follows:

```python
from gurobipy import *

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

# Define the variables
B = m.addVar(lb=0, name="Biotech")
F = m.addVar(lb=0, name="Food")
FI = m.addVar(lb=0, name="Finance")
H = m.addVar(lb=0, name="Healthcare")

# Set the objective function to maximize
m.setObjective(0.04*B + 0.06*F + 0.08*FI + 0.10*H, GRB.MAXIMIZE)

# Add constraints
m.addConstr(B + F + FI + H <= 1000000, "Total_Investment")
m.addConstr(H <= B, "Healthcare_Biotech_Constraint")
m.addConstr(F <= FI, "Food_Finance_Constraint")
m.addConstr(H <= 300000, "Max_Healthcare_Investment")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Objective:", m.objVal)
```